N Following five branches CBCS Sem VII VIII Rev 2019 C Scheme 1 Syllabus Mumbai University

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AC – 01/11/2023
Item No. – 6.12(N)





University of Mumbai





Syllabus for
Bachelor of Engineering
in
• Computer Science and Engineering (Data Science)
• Computer Science and Engineering (Artificial Intelligence and
Machine Learning)
• Artificial Intelligence and Data Science
• Artificial Intelligence and Machine Learning
• Data Engineering
Semester – VII & VIII
Choice Based Credit System
REV- 2019 ‘C’ Scheme
(With effect from Academic Year 20 23 – 24)

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University of Mumbai



Syllabus for Approval
Sr.
No. Heading
Particulars

1 Title of Course
Bachelor of Engineering in
• Computer Science and Engineering (Data
Science)
• Computer Science and Engineering
(Artificial Intelligence and Machine
Learning)
• Artificial Intelligence and Data Science
• Artificial Intelligence and Machine Learning
• Data Engineering


2 Eligibility
After Passing Second Year Engineering as per
The Ordinance 0.6243

3 Standards of Passing




40%
4 Ordinance / Regulations (if any) Ordinance 0.6243
5 No. of years/Semesters 4 years / 8 semesters

6 Level Under Graduation
7 Pattern Semester

8 Status New
REV-2019 ‘C’ Scheme

9 To be implemented from Academic
Year With effect from Academic Year: 2023 -2024




Dr. Deven Shah Dr. Shivram Garje
Offg. Associate Dean Offg. Dean
Faculty of Science and Technology Faculty of Science and Technology

Page 5

Preamble

To meet the challenge of ensuring excellence in engineering education, the issue of quality needs to be
addressed, debated and taken forward in a systematic manner. Accreditation is the principal means of
quality assurance in higher education. The major emphasis of accreditation process is to measure the
outcomes of the program that is being accredited. In line with this Faculty of Science and Tech nology
(in particular Engineering) of University of Mumbai has taken a lead in incorporating philosophy of
outcome based education in the process of curriculum development.
Faculty resolved that course objectives and course outcomes are to be clearly defined for each course,
so that all faculty members in affiliated institutes understand the depth and approach of course to be
taught, which will enhance learner’s learning process. Choice based Credit and grading system enables
a much -required sh ift in focus from teacher -centric to learner -centric education since the workload
estimated is based on the investment of time in learning and not in teaching. It also focuses on
continuous evaluation which will enhance the quality of education. Credit ass ignment for courses is
based on 15 weeks teaching learning process, however content of courses is to be taught in 13 weeks
and remaining 2 weeks to be utilized for revision, guest lectures, coverage of content beyond syllabus
etc.
There was a concern that the earlier revised curriculum more focused on providing information and
knowledge across various domains of the said program, which led to heavily loading of students in
terms of direct contact hours. In this regard, faculty of science and te chnology resolved that to minimize
the burden of contact hours, total credits of entire program will be of 170, wherein focus is not only on
providing knowledge but also on building skills, attitude and self learning. Therefore in the present
curriculum sk ill based laboratories and mini projects are made mandatory across all disciplines of
engineering in second and third year of programs, which will definitely facilitate self learning of
students. The overall credits and approach of curriculum proposed in t he present revision is in line with
AICTE model curriculum.
The present curriculum will be implemented for Second Year of Engineering from the academic year
2021 -22. Subsequently this will be carried forward for Third Year and Final Year Engineering in the
academic years 2022 -23, 2023 -24, respectively.




Dr. S.K. Ukarande Dr Anuradha Muzumdar
Associate Dean Dean
Faculty of Science and Technology Faculty of Science and Technology
University of Mumbai University of Mumbai

Page 6

Incorporation and Implementation of Online Contents
fromNPTEL/ Swayam Platform




The curriculum revision is mainly focused on knowledge component, skill based activities
and project based activities. Self learning opportunities are provided to learners. In the revision
process this time in particular Revised syllabus of ‘C’ scheme wherever possible additional
resource links of platforms such as NPTEL, Swayam are appropriately provided. In an earlier
revision of curriculum in the year 2012 and 2016 in Revised scheme ‘A' and ‘B' respectively,
efforts were made to use online contents more appropriately as additional learning materials to
enhance learning of students.
In the current revision based on the recommendation of AICTE model curriculum overall cre dits
are reduced to 171, to provide opportunity of self learning to learner. Learners are now getting
sufficient time for self learning either through online courses or additional projects for enhancing
their knowledge and skill sets.
The Principals/ HoD’s / Faculties of all the institute are required to motivate and encourage learners
to use additional online resources available on platforms such as NPTEL/ Swayam. Learners can
be advised to take up online courses, on successful completion they are required to submit
certification for the same. This will definitely help learners to facilitate their enhanced learning
based on their interest.





Dr. S.K.Ukarande Dr Anuradha Muzumdar
Associate Dean Dean
Faculty of Science and Technology Faculty of Science and Technology
University of Mumbai University of Mumbai

Page 7

Preface by Board of Studies in
Computer Engineering
Dear Students and Teachers, we, the members of Board of Studies Computer Engineering, are very happy to
present Fourth Year C omputer Engineering Specialization in Data Science, Data Engineering, Artificial
Intelligence and Machine leaning syllabus effective from the Academic Year 2021 -22 (REV -2019’C’
Scheme). We are sure you will find this syllabus interesting, challenging, fulfill certain needs and expectations.

Emerging Programs in the field of Computer Engineering is one of the most sought -after courses amongst
engineering students. The syllabus needs revision in terms of preparing the student for the profess ional
scenario relevant and suitable to cater the needs of industry in present day context. The syllabus focuses on
providing a sound theoretical background as well as good practical exposure to students in the relevant areas.
It is intended to provide a modern, industry -oriented education in Computer Engineering. It aims at producing
trained professionals who can successfully acquaint with the demands of the industry worldwide. They obtain
skills and experience in up-to-date the knowledge to analysis, design, implementation, validation, and
documentation of computer software and systems.

The revised syllabus is finalized through a brain storming session attended by Heads of Departments or senior
faculty from the Department of Computer Engineering of the affiliated Institutes of the Mumbai University.
The syllabus falls in line with the objectives of affiliating University, AICTE, UGC, and various accreditation
agencies by keeping an eye on the technological develo pments, innovations, and industry requirements.

The salient features of the revised syllabus are:
1. Reduction in credits to 170 is implemented to ensure that students have more time for
extracurricular activities, innovations, and research.
2. The department Optional Courses will provide the relevant specialization within the branch to a
student.
3. Introduction of Skill Based Lab and Mini Project to showcase their talent by doing innovative
projects that strengthen their profile and increases the chan ce of employability.
4. Students are encouraged to take up part of course through MOOCs platform SWAYAM
We would like to place on record our gratefulness to the faculty, students, industry experts and stakeholders
for having helped us in the formulation of this syllabus.
Board of Studies in Computer Engineering
Prof. Sunil Bhirud : Chairman
Prof. SunitaPatil : Member
Prof. Leena Ragha : Member
Prof. Subhash Shinde : Member
Prof .Meera Narvekar : Member
Prof. Suprtim Biswas : Member
Prof. Sudhir Sawarkar : Member
Prof. Dayanand Ingle : Member
Prof. Satish Ket : Member

Page 8

Program Structure for Fourth Year CSE (AIML), CSE (DS) AI&DS, DE, AI&ML
UNIVERSITY OF MUMBAI (With Effect from 2023 -2024)
Semester VII

Course
Code
Course Name Teaching Scheme
(Contact Hours)
Credits Assigned
Theory Pract.
Tut. Theory Pract. Total
CSC701 Deep Leaning 3 -- 3 -- 3
CSC702 Big Data Analytics 3 -- 3 3
CSDO
701X Department Level
Optional Course -3 3 -- 3 -- 3
CSDO
702X Department Level
Optional Course -4 3 -- 3 -- 3
ILO
701X Institute Level Optional
Course -1 3 -- 3 -- 3
CSL701 Deep Leaning Lab -- 2 -- 1 1
CSL702 Big Data Analytics Lab -- 2 -- 1 1
CSDOL
701X Department Level
Optional Course -3 Lab -- 2 -- 1 1
CSDOL
702X Department Level
OptionalCourse -4 Lab -- 2 -- 1 1
CSP701 Major Project1 -- 6# -- 3 3
Total 15 14 15 7 22



Course
Code


Course Name Examination Scheme
Theory Term
Work Pract.
& oral Total
Internal
Assessment End
Sem
Exam Exam.
Duration
(in Hrs)
Test
1 Test
2 Avg
CSC701 Deep Leaning 20 20 20 80 3 -- -- 100
CSC702 Big Data Analytics 20 20 20 80 3 -- -- 100
CSDO
701X Department Level
Optional Course -3 20 20 20 80 3 -- -- 100
CSDO
702X Department Level
Optional Course -4 20 20 20 80 3 -- -- 100
ILO
701X Institute Level Optional
Course -1 20 20 20 80 3 -- -- 100
CSL701 Deep Leaning Lab -- -- -- -- -- 25 25 50
CSL702 Big Data Analytics Lab -- -- -- -- -- 25 25 50
CSDOL
701X Department Level
Optional Course -3 Lab 25 - 25
CSDOL
702X Department Level
OptionalCourse -4 Lab -- -- -- -- -- 25 - 25
CSP701 Major Project1 -- -- -- -- -- 50 25 75
Total -- -- 100 400 -- 150 75 725

Page 9

Program Structure for Fourth Year CSE (AIML), CSE (DS) AI&DS, DE, AI&ML
UNIVERSITY OF MUMBAI (With Effect from 2023 -2024)
Semester VIII


Course
Code
Course Name Teaching Scheme
(Contact Hours)
Credits Assigned
Theory Pract.
Tut. Theory Pract. Total
CSC801 Advanced Artificial
Intelligence 3 -- 3 -- 3
CSDO
801X Department Level
Optional Course -5 3 -- 3 -- 3
CSDO
802X Department Level
OptionalCourse -6 3 -- 3 -- 3
ILO
801X Institute Level
OptionalCourse -2 3 -- 3 -- 3
CSL801 Advanced Artificial
Intelligence Lab -- 2 -- 1 1
CSDOL
801X Department Level Optional
Course -5 Lab -- 2 -- 1 1
CSDOL
802X Department Level Optional
Course -6 Lab -- 2 -- 1 1
CSP801 Major Project -2 -- 12# -- 6 6
Total 12 18 12 9 21



Course
Code


Course Name Examination Scheme
Theory Term
Work Pract
& oral Total

Internal Assessment End
Sem
Exam Exam
Duration
(in Hrs)
Test
1 Test
2 Avg
CSC801 Advanced Artificial
Intelligence 20 20 20 80 3 -- -- 100
CSDO8
01X Department Level Optional
Course -5 20 20 20 80 3 -- -- 100
CSDO
802X Department Level Optional
Course -6 20 20 20 80 3 -- -- 100
ILO80X Institute Level Optional
Course -2 20 20 20 80 3 -- -- 100
CSL801 Advanced Artificial
Intelligence Lab -- -- -- -- -- 25 25 50
CSDOL
801X Department Level Optional
Course -5 Lab -- -- -- -- -- 25 25 50
CSDOL
802X Department Level Optional
Course -6 Lab 25 25 50
CSP801 Major Project 2 -- -- -- -- -- 100 50 150
Total -- -- 80 320 -- 175 125 700
Major Project 1 and 2 :
• Students can form groups with minimum 2 (Two) and not more than 4 (Four)
• Faculty Load : In Semester VII – ½ hour per week per project group
In Semester VIII – 1 hour per week per project group

Page 10

Program Structure for Fourth Year CSE (AIML), CSE (DS) AI&DS, DE, AI&ML
UNIVERSITY OF MUMBAI (With Effect from 2023 -2024)
Department and Institute Optional Courses and Labs


Semester Department/
Institute Optional
Courses and Labs
Subject and Labs
















VII
Department Optional
Course -3 CSDO7011: Natural Language Processing
CSDO7012.: AI for Healthcare
CSDO7013: Neural Network & Fuzzy System

Department Optional
Lab -3 CSDOL7011: Natural Language Processing Lab
CSDOL7012.: AI for Healthcare Lab
CSDOL7013: Neural Network & Fuzzy System

Department Optional
Course -4 CSDO7021: User Experience Design with VR
CSDO7022: Blockchain Technologies
CSDO7023: Game Theory for Data Science

Department Optional
Lab -4 CSDOL7021: User Experience Design with VR Lab
CSDOL7022: Blockchain Technologies
CSDOL7023: Game Theory for Data Science


Institute level
Optional
Courses -I ILO7011:Product Lifecycle Management
ILO7012: Reliability Engineering
ILO7013.: Management Information System
ILO7014: Design of Experiments
ILO7015: Operation Research
ILO7016: Cyber Security and Laws
ILO7017: Disaster Management & Mitigation Measures
ILO7018: Energy Audit and Management
ILO7019: Development Engineering

Page 11

Program Structure for Fourth Year CSE (AIML), CSE (DS) AI&DS, DE, AI&ML
UNIVERSITY OF MUMBAI (With Effect from 2023 -2024)
Department and Institute Optional Courses and Labs


Semester Department/
Institute Optional
Courses and Labs
Subject and Labs













VIII
Department Optional
Course -5 CSDO8011: AI for financial & Banking application
CSDO8012: Quantum Computing
CSDO8013: Reinforcement Learning

Department Optional
Lab -5 CSDOL8011: AI for financial & Banking application Lab
CSDOL8012: Quantum Computing Lab
CSDOL8013: Reinforcement Learning Lab

Department Optional
Course -6 CSDO8021: Graph Data Science
CSDO8022: Recommendation Systems
CSDO8023: Social Media Analytic

Department Optional
Lab -6 CSDOL8021: Graph Data Science Lab
CSDOL8022: Recommendation Systems Lab
CSDOL8023: Social Media Analytic Lab


Institute level
Optional
Courses -II ILO8021: Project Management
ILO8022: Finance Management
ILO8023: Entrepreneurship Development and Management
ILO8024: Human Resource Management
ILO8025: Professional Ethics and CSR
ILO8026: Research Methodology
ILO8027: IPR and Patenting
ILO8028: Digital Business Management
ILO8029: Environmental Management

Page 12

Course Code: Course Title Credit
CSC701 Deep Learning 3


Prerequisite: Basic mathematics and Statistical concepts, Linear algebra, Machine
Learning
Course Objectives:
1 To learn the fundamentals of Neural Network.
2 To gain an in-depth understanding of training Deep Neural Networks.
3 To acquire knowledge of advanced concepts of Convolution Neural Networks,
Autoencoders and Recurrent Neural Networks.
4 Students should be familiar with the recent trends in Deep Learning.
Course Outcomes:
1 Gain basic knowledge of Neural Networks.
2 Acquire in depth understanding of training Deep Neural Networks.
3 Design appropriate DNN model for supervised, unsupervised and sequence learning
applications.
4 Gain familiarity with recent trends and applications of Deep Learning.

Modul
e Content 39Hrs
1 Fundamentals of Neural Network 4
1.1 History of Deep Learning, Deep Learning Success Stories, Multilayer
Perceptrons (MLPs), Representation Power of MLPs, Sigmoid Neurons,
Gradient Descent, Feedforward Neural Networks, Representation Power
of Feedforward Neural Networks
1.2 Deep Networks: Three Classes of Deep Learning Basic Terminologies
of Deep Learning
2 Training, Optimization and Regularization of Deep Neural
Network 10
2.1 Training Feedforward DNN
Multi Layered Feed Forward Neural Network, Learning Factors,
Activation functions: Tanh, Logistic, Linear, Softmax, ReLU, Leaky
ReLU, Loss functions: Squared Error loss, Cross Entropy, Choosing
output function and loss function
2.2 Optimization
Learning with backpropagation, Learning Parameters: Gradient
Descent (GD), Stochastic and Mini Batch GD, Momentum Based GD,
Nesterov Accelerated GD, AdaGrad, Adam, RMSProp
2.3 Regularization
Overview of Overfitting, Types of biases, Bias Variance Tradeoff
Regularization Methods: L1, L2 regularization, Parameter sharing,
Dropout, Weight Decay, Batch normalization, Early stopping, Data
Augmentation, Adding noise to input and output
3 Autoencoders: Unsupervised Learning 6
3.1 Introduction, Linear Autoencoder, Undercomplete Autoencoder,
Overcomplete Autoencoders, Regularization in Autoencoders

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3.2 Denoising Autoencoders, Sparse Autoencoders, Contractive
Autoencoders
3.3 Application of Autoencoders: Image Compression
4 Convolutional Neural Networks (CNN): Supervised Learning 7
4.1 Convolution operation, Padding, Stride, Relation between input, output
and filter size, CNN architecture: Convolution layer, Pooling Layer,
Weight Sharing in CNN, Fully Connected NN vs CNN, Variants of
basic Convolution function, Multichannel convolution operation,2D
convolution.
4.2 Modern Deep Learning Architectures:
LeNET: Architecture, AlexNET: Architecture, ResNet : Architecture
5 Recurrent Neural Networks (RNN) 8
5.1 Sequence Learning Problem, Unfolding Computational graphs,
Recurrent Neural Network, Bidirectional RNN, Backpropagation
Through Time (BTT), Limitation of “ vanilla RNN” Vanishing and
Exploding Gradients, Truncated BTT
5.2 Long Short Term Memory(LSTM): Selective Read, Selective write,
Selective Forget, Gated Recurrent Unit (GRU)
6 Recent Trends and Applications 4
6.1 Generative Adversarial Network (GAN): Architecture
6.2 Applications: Image Generation, DeepFake


Textbooks:
1 Ian Goodfellow, Yoshua Bengio, Aaron Courville. ―Deep Learningǁ, MIT Press Ltd, 2016
2 Li Deng and Dong Yu, ―Deep Learning Methods and Applicationsǁ, Publishers Inc.
3 Satish Kumar "Neural Networks A Classroom Approach" Tata McGraw -Hill.
4 JM Zurada ―Introduction to Artificial Neural Systemsǁ, Jaico Publishing House
5 M. J. Kochenderfer, Tim A. Wheeler. ―Algorithms for Optimizationǁ, MIT Press.
References:
1 Deep Learning from Scratch: Building with Python from First Principles - Seth Weidman by
O`Reilley
2 François Chollet. ―Deep learning with Python ―(Vol. 361). 2018 New York: Manning.
3 Douwe Osinga. ―Deep Learning Cookbookǁ, O‘REILLY, SPD Publishers, Delhi.
4 Simon Haykin, Neural Network - A Comprehensive Foundation - Prentice Hall
International, Inc
5 S.N.Sivanandam and S.N.Deepa, Principles of soft computing -Wiley India

Assessment :
Internal Assessment:
The assessment consists of two class tests of 20 marks each. The first class test is to be
conducted when approx. 40% syllabus is completed and second class test when additional 40%
syllabus is completed. Duration of each test shall be one hour.
End Semester Theory Examination:
1 Question paper will comprise a total of six questions.
2 All questions carry equal marks.
3 Question 1 and question 6 will have questions from all modules. Remaining 4 questions
will be based on the remaining 4 modules.

Page 14

4 Only four questions need to be solved.
5 In question paper weightage of each module will be proportional to the number of
respective lecture hours as mentioned in the syllabus.


Useful Links
1 http://www.cse.iitm.ac.in/~miteshk/CS6910.html
2 https://nptel.ac.in/courses/106/106/106106184/
3 https://www.deeplearningbook.org/

Page 15

Course Code Course/Subject Name Credits
CSC702 Big Data Analytics 3



Prerequisite: Some prior knowledge about Java programming, Basics of SQL, Data mining and
machine learning methods would be beneficial.
Course Objectives:
1 To provide an overview of an exciting growing field of big data analytics.
2 To introduce programming skills to build simple solutions using big data technologies such as
MapReduce and scripting for NoSQL, and the ability to write parallel algorithms for
multiprocessor execution
3 To teach the fundamental techniques and principles in achieving big data analytics with
scalability and streaming capability.
4 To enable students to have skills that will help them to solve complex real-world problems in
decision support.
5 To provide an indication of the current research approaches that is likely to provide a basis for
tomorrow's solutions.
Course Outcomes:
1 Understand the key issues in big data management and its associated applications for
business decisions and strategy.
2 Develop problem solving and critical thinking skills in fundamental enabling techniques like
Hadoop, Map reduce and NoSQL in big data analytics.
3 Collect, manage, store, query and analyze various forms of Big Data.
4 Interpret business models and scientific computing paradigms,and apply software tools for
big data analytics.
5 Adapt adequate perspectives of big data analytics in various applications like recommender
systems, social media applications etc.
6 Solve Complex real world problems in various applications like recommender systems,
social media applications, health and medical systems, etc.

Page 16

Module Detailed Contents Hours
01 Introduction to Big Data & Hadoop
1.1 Introduction to Big Data, 1.2 Big Data characteristics, types of Big
Data, 1.3 Traditional vs. Big Data business approach, 1.4 Case Study of
Big Data Solutions. 1.5 Concept of Hadoop 1.6 Core Hadoop
Components; Hadoop Ecosystem 06
02 Hadoop HDFS and Map Reduce
2.1 Distributed File Systems: Physical Organization of Compute Nodes,
Large -Scale File-System Organization. 2.2 MapReduce: The Map Tasks,
Grouping by Key, The Reduce Tasks, Combiners, Details of
MapReduce Execution, Coping With Node Failures. 2.3 Algorithms
Using MapReduce: Matrix -Vector Multiplication by MapReduce,
Relational -Algebra Operations, Computing Selections by MapReduce,
Computing Projections by MapReduce, Union, Intersection, and
Difference by MapReduce 2.4 Hadoop Limitations s. 10
03 NoSQL
3.1 Introduction to NoSQL, NoSQL Business Drivers, 3.2 NoSQL Data
Architecture Patterns: Key -value stores, Graph stores, Column family
(Bigtable)stores, Document stores, Variations of NoSQL architectural
patterns, NoSQL Case Study 3.3 NoSQL solution for big data,
Understanding the types of big data problems; Analyzing big data with a
shared -nothing architecture; Choosing distribution models: master -slave
versus peer-to-peer; NoSQL systems to handle big data problems.
peer-to-peer; Four ways that NoSQL systems handle big data problems 06
04 Mining Data Streams
4.1 The Stream Data Model: A Data -Stream -Management System,
Examples of Stream Sources, Stream Queries, Issues in Stream
Processing. 4.2 Sampling Data techniques in a Stream 4.3 Filtering
Streams: Bloom Filter with Analysis. 4.4 Counting Distinct Elements in
a Stream, Count -Distinct Problem, Flajolet -Martin Algorithm,
Combining Estimates, Space Requirements 4.5 Counting Frequent Items
in a Stream, Sampling Methods for Streams, Frequent Itemsets in
Decaying Windows. 4.6 Counting Ones in a Window: The Cost of Exact
Counts, The Datar -Gionis -Indyk -Motwani Algorithm, Query Answering
in the DGIM Algorithm, Decaying Windows. 12
05 Finding Similar Items and Clustering
5.1 Distance Measures: Definition of a Distance Measure, Euclidean
Distances, Jaccard Distance, Cosine Distance, Edit Distance, Hamming
Distance. 5.2 CURE Algorithm, Stream -Computing , A
Stream -Clustering Algorithm, Initializing & Merging Buckets,
Answering Queries. 08
06 Real -Time Big Data Models
6.1 PageRank Overview, Efficient computation of PageRank: PageRank
Iteration Using MapReduce, Use of Combiners to Consolidate the
Result Vector. 6.2 A Model for Recommendation Systems,
Content -Based Recommendations, Collaborative Filtering. 6.3 Social 10

Page 17

Networks as Graphs, Clustering of Social -Network Graphs, Direct
Discovery of Communities in a social graph.



Textbooks:
1 Anand Rajaraman and Jeff Ullman ―Mining of Massive Datasetsǁ, Cambridge University
Press,
2 Alex Holmes ―Hadoop in Practiceǁ, Manning Press, Dreamtech Press.
3 Dan Mcary and Ann Kelly ―Making Sense of NoSQLǁ – A guide for managers and the rest
of us, Manning Press.
References:
1 Bill Franks , ―Taming The Big Data Tidal Wave: Finding Opportunities In Huge Data
Streams With Advanced Analyticsǁ, Wiley
2 Chuck Lam, ―Hadoop in Actionǁ, Dreamtech Press
3 Jared Dean, ―Big Data, Data Mining, and Machine Learning: Value Creation for Business
Leaders and Practitionersǁ, Wiley India Private Limited, 2014.
4 Jiawei Han and Micheline Kamber, ―Data Mining: Concepts and Techniquesǁ, Morgan
Kaufmann Publishers, 3rd ed, 2010.
5 Lior Rokach and Oded Maimon, ―Data Mining and Knowledge Discovery Handbookǁ,
Springer, 2nd edition, 2010.
6 Ronen Feldman and James Sanger, ―The Text Mining Handbook: Advanced Approaches in
Analyzing Unstructured Dataǁ, Cambridge University Press, 2006.
7 Vojislav Kecman, ―Learning and Soft Computingǁ, MIT Press, 2010




Assessment :
Internal Assessment:
The assessment consists of two class tests of 20 marks each. The first class test is to be
conducted when approx. 40% syllabus is completed and second class test when additional 40%
syllabus is completed. Duration of each test shall be one hour.
End Semester Theory Examination:
1 Question paper will comprise a total of six questions.
2 All questions carry equal marks.

Page 18

3 Question 1 and question 6 will have questions from all modules. Remaining 4 questions
will be based on the remaining 4 modules.
4 Only four questions need to be solved.
5 In question paper weightage of each module will be proportional to the number of
respective lecture hours as mentioned in the syllabus.

Page 19

Course Code: Course Title Credit
CSDO7011 Natural Language Processing 3


Prerequisite: Artificial Intelligence and Machine Learning, Basic knowledge of Python
Course Objectives:
1 To understand natural language processing and to learn how to apply basic algorithms in this field
2 To get acquainted with the basic concepts and algorithmic description of the main language levels:
morphology, syntax, semantics, and pragmatics
3 To design and implement various language models and POS tagging techniques
4 To design and learn NLP applications such as Information Extraction, Question answering
5 To design and implement applications based on natural language processing
Course Outcomes:
1 To have a broad understanding of the field of natural language processing
2 To design language model for word level analysis for text processing
3 To design various POS tagging techniques
4 To design, implement and test algorithms for semantic analysis
5 To develop basic understanding of Pragmatics and to formulate the discourse segmentation and
anaphora resolution
6 To apply NLP techniques to design real world NLP applications

Module Content Hrs
1 Introduction 4
1.1 Origin & History of NLP, The need of NLP, Generic NLP System, Levels
of NLP, Knowledge in Language Processing, Ambiguity in Natural
Language, Challenges of NLP, Applications of NLP.
2 Word Level Analysis 8
2.1 Tokenization, Stemming, Segmentation, Lemmatization, Edit Distance,
Collocations, Finite Automata, Finite State Transducers (FST), Porter

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Stemmer, Morphological Analysis, Derivational and Reflectional
Morphology, Regular expression with types.
2.2 N –Grams, Unigrams/Bigrams Language Models, Corpora, Computing the
Probability of Word Sequence, Training and Testing.
3 Syntax analysis 8
3.1 Part-Of-Speech Tagging (POS) - Open and Closed Words. Tag Set for
English (Penn Treebank), Rule Based POS Tagging, Transformation Based
Tagging, Stochastic POS Tagging and Issues –Multiple Tags & Words,
Unknown Words.
3.2 Introduction to CFG, Hidden Markov Model (HMM), Maximum Entropy,
And Conditional Random Field (CRF).
4 Semantic Analysis 8
4.1 Introduction, meaning representation; Lexical Semantics; Corpus study;
Study of Various language dictionaries like WordNet, Babelnet; Relations
among lexemes & their senses –Homonymy, Polysemy, Synonymy,
Hyponymy; Semantic Ambiguity
4.2 Word Sense Disambiguation (WSD); Knowledge based approach (Lesk‘s
Algorithm), Supervised (Naïve Bayes, Decision List), Introduction to
Semi -supervised method (Yarowsky), Unsupervised (Hyperlex)
5 Pragmatic & Discourse Processing 6
5.1 Discourse: Reference Resolution, Reference Phenomena, Syntactic &
Semantic constraint on coherence; Anaphora Resolution using Hobbs and
Cantering Algorithm
6 Applications (preferably for Indian regional languages) 5
6.1 Machine Translation, Information Retrieval, Question Answers System,
Categorization, Summarization, Sentiment Analysis, Named Entity
Recognition.
6.2 Linguistic Modeling – Neurolinguistics Models - Psycholinguistic Models –
Functional Models of Language – Research Linguistic Models - Common
Features of Modern Models of Language.

Page 21

Textbooks:
1 Daniel Jurafsky, James H. and Martin, Speech and Language Processing, Second Edition,
Prentice Hall, 2008.
2 Christopher D.Manning and HinrichSchutze, Foundations of Statistical Natural Language
Processing, MIT Press, 1999.
References:
1 Siddiqui and Tiwary U.S., Natural Language Processing and Information Retrieval, Oxford
University Press, 2008.
2 Daniel M Bikel and ImedZitouni ― Multilingual natural language processing applications: from
theory to practice, IBM Press, 2013.
3 Nitin Indurkhya and Fred J. Damerau, ―Handbook of Natural Language Processing, Second
Edition, Chapman and Hall/CRC Press, 2010.



Assessment :
Internal Assessment:
Assessment consists of two class tests of 20 marks each. The first class test is to be conducted when
approx. 40% syllabus is completed and second class test when additional 40% syllabus is completed.
Duration of each test shall be one hour.
End Semester Theory Examination:
1 Question paper will comprise of total six questions.
2 All question carries equal marks
3 Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then
part (b) will be from any module other than module 3)
4 Only Four question need to be solved
5 In question paper weightage of each module will be proportional to number of respective lecture
hours as mention in the syllabus


Useful Links

Page 22

1 https://onlinecourses.nptel.ac.in/noc21_cs102/preview
2 https://onlinecourses.npt el.ac.in/noc20_cs87/preview
3 https://nptel.ac.in/courses/106105158

Page 23

Course Code Course/Subject Name Credits
CSDO7012 AI for Healthcare 3

Course Prerequisites:
Artificial Intelligence, Machine Learning
Course Objectives: The course aims
1 To understand the need and significance of AI and ML for Healthcare.
2 To study advanced AI algorithms for Healthcare.
3 To learn Computational Intelligence techniques .
4 To understand evaluation metrics and ethics in intelligence for Healthcare systems,
5 To learn various NLP algorithms and their application in Healthcare,
6 To investigate the current scope, implications of AI and ML for developing futuristic Healthcare Applications.
Course Outcomes:
After successful completion of the course, the student will be able to:
1 Understand the role of AI and ML for handling Healthcare data.
2 Apply Advanced AI algorithms for Healthcare Problems.
3 Learn and Apply various Computational Intelligence techniques for Healthcare Application.
4 Use evaluation metrics for evaluating healthcare systems.
5 Develop NLP applications for healthcare using various NLP Techniques..
6 Apply AI and ML algorithms for building Healthcare Applications


Module Topics Hou
rs
.
1 Introduction 06
1.1 Overview of AI , ML and DL ,A Multifaceted Discipline, Applications of AI in Healthcare -
Prediction, Diagnosis, personalized treatment and behavior modification, drug
discovery, followup care etc,
1.2 Realizing potential of AI in healthcare, Healthcare Data - Use Cases.
2 AI, ML, Deep Learning and Data Mining Methods for Healthcare 08
2.1 Knowledge discovery and Data Mining, ML, Multi classifier Decision Fusion, Ensemble
Learning, Meta -Learning and other Abstract Methods.
2.2 Evolutionary Algorithms, Illustrative Medical Application -Multiagent Infectious Disease
Propagation and Outbreak Prediction, Automated Amblyopia Screening System etc.
2.3 Computational Intelligence Techniques, Deep Learning, Unsupervised learning,
dimensionality reduction algorithms.
3 Evaluating learning for Intelligence 04
3.1 Model development and workflow, evaluation metrics, Parameters and
Hyperparameters, Hyperparameter tuning algorithms, multivariate testing, Ethics
of Intelligence.
4 Natural Language Processing in Healthcare 08
4.1 NLP tasks in Medicine, Low-level NLP components, High level NLP components, NLP Methods.
4.2 Clinical NLP resources and Tools, NLP Applications in Healthcare. Model Interpretability
using Explainable AI for NLP applications.
5 Intelligent personal Health Record 05
5.1 Introduction, Guided Search for Disease Information, Recommending SCA's.

Page 24

Recommending HHP's , Continuous User Monitoring.
6 Future of Healthcare using AI 08
6.1 Evidence based medicine, Personalized Medicine, Connected Medicine, Digital Health
and Therapeutics, Conversational AI, Virtual and Augmented Reality, Blockchain for
verifying supply chain, patient record access, Robot - Assisted Surgery, Smart Hospitals,
Case Studies on use of AI and ML for Disease Risk Diagnosis from patient data,
Augmented reality applications for Junior doctors.
6.2 Blockchain for verifying supply chain, patient record access, Robot - Assisted Surgery,
Smart Hospitals, Case Studies on use of AI and ML for Disease Risk Diagnosis from
patient data, Augmented reality applications for Junior doctors.
Total 39



Textbooks:
1 Arjun Panesar, "Machine Learning and AI for Healthcare”, A Press.
2 Arvin Agah, "Medical applications of Artificial Systems ", CRC Press

References:
1 Erik R. Ranschaert Sergey Morozov Paul R. Algra, “Artificial Intelligence in medical
Imaging - Opportunities, Applications and Risks”, Springer
2 Sergio Consoli Diego Reforgiato Recupero Milan Petković,“Data Science for Healthcare -
Methodologies and Applications”, Springer
3 Dac-Nhuong Le, Chung Van Le, Jolanda G. Tromp, Gia Nhu Nguyen, “Emerging technologies for
health and medicine”, Wiley.
4 Ton J. Cleophas • Aeilko H. Zwinderman, “Machine Learning in Medicine - Complete
Overview”, Springer

Assessment :
Internal Assessment:
Assessment consists of two class tests of 20 marks each. The first class test is to be conducted
when approx. 40% syllabus is completed and second class test when additional 40% syllabus is
completed. Duration of each test shall be one hour.

End Semester Theory Examination:
1 Question paper will comprise of total six questions.
2 All question carries equal marks
3 Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3
then part (b) will be from any module other than module 3)
4 Only Four question need to be solved.
5 In question paper weightage of each module will be proportional to number of respective
lecture hours as mention in the syllabus.

Page 25

Course Code: Course Title Credit
CSDO7013 Neural Networks and Fuzzy Systems 3


Prerequisite: Engineering Mathematics, Data Structures and Algorithm, Python Programming
Course Objectives:
1 To relate to the basic terminologies with respect to Fuzzy set theory.
2 To analyze and interpret fuzzy logic principles, relations and operations.
3 To recognize various components of Associative Memory Networks.
4 To have basic understanding of Unsupervised learning through Networks.
5 To understand Special networks and its applications in soft computing.
6 To infer the significance of Hybrid computing.

Course Outcomes: After successful completion of the course student will be able to
1 Acquire basic knowledge of fuzzy set theory properties and relations.
2 Implement Fuzzy operations towards Fuzzy -rule creations.
3 Gain familiarity with the training and implementation of Associative Memory
Network.
4 Understand the architecture and basics components of Unsupervised learning
networks.
5 Analyze the significance and working of the Special Networks.
6 Interpret Hybrid System to analyze the Principles of Soft computing in Neuro -Fuzzy
applications.
Module Content Hrs
1.0 Fuzzy Set Theory 07
1.1 Introduction to soft and hard computing Fuzzy Sets:
Basic definition and terminology of fuzzy sets, Classic set
operations; Fuzzy set operations - Union, Intersection, complement,
Difference; Properties of fuzzy sets.

1.2 Fuzzy relations:
Cartesian product of relation, Classica Relation, Cardinality of
fuzzy relations, Operations on Fuzzy relations, Properties of Fuzzy
relations, Fuzzy composition, Tolerance and Equivalence
Relationship.

1.3 Membership Functions:
Features of Membership Functions, Fuzzification, Methods of
membership value assignments.
2.0 Fuzzy Rules, Reasoning, and Inference System 08

Page 26

2.1 Defuzzification:
Lambda -Cuts for Fuzzy Sets; Lambda -Cuts for Fuzzy Relations;
Defuzzification methods: Max-Membership Principles, Centroid
Method, Weighted Average Method, Mean -Max Membership, Center
of Sums, Center of Largest Area, First of Maxima.
2.2 Fuzzy Arithmetic and Rules:
Fuzzy arithmetic, Fuzzy measures, Measures of Fuzziness, Truth
Value and Tables in Fuzzy Logic, Fuzzy Propositions, Formation of
rules, Decomposition of rules, Fuzzy Reasoning.
2.3 Fuzzy Inference System (FIS):
Mamdani FIS, Sugeno FIS, Comparison between Mamdani and
Sugeno FIS.
3.0 Associative Memory Networks 06
3.1 Introduction:
Basics of associative memory networks, Training algorithms for
Pattern Association.
3.2 Types of Networks:
Radial basis function network : architecture training algorithm , Auto -
associative Memory Network – Architecture, Flowchart of training
process, Training algorithm, Testing algorithm, Hetero - associative
Memory Network - Architecture and Testing algorithm, Bidirectional
Associative Memory(BAM) Network - Architecture, Discrete BAM,
Continuous BAM.
4.0 Unsupervised Learning Networks 08
4.1 Introduction
Fixed weight competitive nets, Maxnet, Maxican net, Hamming
Network

4.2 Kohonen Self- Organizing Feature Maps:
Basic concepts, Architecture, Flowchart, Algorithms, Kohonen
Self-Organizing Motor map
Training algorithm.

4.3 Adaptive resonance Theory:
Architecture, Fundamental Operating principles, a Algorithms,
Adaptive Resonance Theory I – Architecture, Flowchart of Training
process, Training algorithm, Adaptive Resonance Theory 2 -
Architecture, Algorithm, Flowchart, Training algorithm, Sample
Values of Parameter.
5.0 Special Network 05
5.1 Introduction:
Boltzmann Machine, Gaussian Machine, Probabilistic neural nets
Spatio -Temporal connection network model, Ensemble neural model
Extreme learning machine models, Online, Pruned, Improved
Application of ELM

Page 27

6.0 Hybrid Computing 05
6.1 Neuro -Fuzzy Hybrid Systems:
Introduction to Neuro -Fuzzy systems, Comparison of Fuzzy systems
and Neural networks, Characteristics of Neuro -Fuzzy systems,
Classification of Neuro -Fuzzy systems. Introduction to Adaptive
Neuro -Fuzzy Inference System (ANIFS), ANFS Architecture,
Constraints of ANFIS, ANFIS as a Universal Approximator.

Textbooks:
1 S.N. Sivanandan and S.N. Deepa, Principles of Soft Computing, Wiley India, 2007, ISBN:
10: 81- 265-1075 -7.
2 J.-S. R. Jang, C. –T. Sun, E. Mizutani, Neuro -Fuzzy and Soft Computing, A Computational
Approach to Learning and Machine Intelligence, PHI Learning Private Limited -2014
3 Neural Networks: A Classroom Approach, Satish Kumar, Tata McGraw -Hill Education,
2004/2007
4 Simon Haykin, Neural Networks A Comprehensive Foundation, Second Edition, Pearson
Education -2004
5 David E. Goldberg, Genetic Algorithms, in search, optimization and Machine Learning,
Pearson

References:
1 Anupam Shukla, Ritu Tiwari, Rahul Kala, Real Life Applications of Soft Computing, CRC
Press, Taylor & Francis Group, 2010.
2 Genetic Algorithms and Genetic Programming Modern Concepts and Practical Applications
© 2009 Michael Affenzeller, Stephan Winkler, Stefan Wagner, and Andreas Beham, CRC
Press
3 Laurene V. Fausett, Fundamentals of Neural Networks: Architectures, Algorithms And
Applications, Pearson

Digital References:
https://onlinecourses.nptel.ac.in/noc22_ee21/preview
https://onlinecourses.nptel.ac.in/noc23_ge15/preview

Assessment :
Internal Assessment:
Assessment consists of two class tests of 20 marks each. The first class test is to be conducted
when approx. 40% syllabus is completed and second class test when additional 40% syllabus is
completed. Duration of each test shall be one hour.

End Semester Theory Examination:

Page 28

1 Question paper will comprise of total six questions.
2 All question carries equal marks
3 Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3
then part (b) will be from any module other than module 3)
4 Only Four question need to be solved.
5 In question paper weightage of each module will be proportional to number of respective
lecture hours as mention in the syllabus.

Page 29

Course Code: Course Title Credit
CSDO7021 User Experience Design with VR 3


Prerequisite: Web Technologies; Software Engineering
Course Objectives:
1 To study and understand importance of user experience design principles
2 To understand elements of user experience design
3 To encourage students to participate in designing futuristic applications
4 To understand the need and significance of Virtual Reality
5 To understand the technical and engineering aspects of virtual reality systems
Course Outcomes:
1 To Apply principles of user experience
2 To apply emerging and established technologies to enhance User Experience design
3 To create interface for international standards with ethics
4 To evaluate user experience.
5 Describe how VR systems work and list the applications of VR
6 Design and implementation of the hardware that enables VR systems to be built

Module Content Hrs
1 Introduction 04
1.1 Introduction to interface design, Understanding and conceptualizing
Interface, understanding user’s conceptual cognition, Core Elements of
User Experience, Working of UX elements
2 The UX Design Process – Understanding Users & Structure: 08
2.1 Defining the UX, Design Process and Methodology, Understanding user
requirements and goals, Understanding the Business Requirements/Goals,
User research, mental models, wireframes, prototyping, usability testing.
2.2 Visual Design Principles , Information Design and Data Visualization
Interaction Design, UI Elements and Widgets, Screen Design and Layouts

Page 30

3 UX Design Process: Prototype and Test 06
3.1 Testing your Design, Usability Testing, Types of Usability Testing ,
Usability Testing Process, Preparing and planning for the Usability Tests,
3.2 Prototype your Design to Test, Introduction of prototyping tools,
conducting Usability Test, communicating Usability Test Results
4 UX Design Process: Iterate/ Improve and Deliver 05
4.1 Understanding the Usability Test, findings, Applying the Usability Test,
feedback in improving the design.
4.2 Communication with implementation team. UX Deliverables to be given to
implementation team
5 Introduction to Virtual Reality 08
5.1 Defining Virtual Reality, History of VR, Human Physiology and Perception,
Key Elements of Virtual Reality Experience, Virtual Reality System,
Interface to the Virtual World -Input & output - Visual, Aural &
Haptic Displays, Applications of Virtual Reality
5.2 Representation of the Virtual World, Visual Representation in VR, Aural
Representation in VR and Haptic Representation in VR
6 Applying Virtual Reality 08
6.1 Virtual reality: the medium, Form and genre, What makes an application a
good candidate for VR, Promising application fields, Demonstrated benefits
of virtual reality, More recent trends in virtual reality application
development, A framework for VR application development





Textbooks:
1 Interaction Design, Beyond Human Computer Interaction, Rogers, Sharp, Preece Wiley India
Pvt Ltd.
2 The essentials of Interaction Design, Alan Cooper, Robert Reimann, David Cronin
3 Designing The user Interface by Shneiderman, Plaisant, Cohen, Jacobs Pearson
References:

Page 31

1 The Elements of User Experience by Jesse James Garrett
2 Don’t make me think, by Steve Krug
3 Observing the User Experience: A Practitioner's Guide to User Research by Mike Kuniavsky


Assessment:
Internal Assessment:
Assessment consists of two class tests of 20 marks each. The first class test is to be conducted when
approx. 40% syllabus is completed and second class test when additional 40% syllabus is completed.
Duration of each test shall be one hour.
End Semester Theory Examination:
1 Question paper will comprise of total six questions.
2 All question carries equal marks
3 Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then
part (b) will be from any module other than module 3)
4 Only Four question need to be solved
5 In question paper weightage of each module will be proportional to number of respective lecture
hours as mention in the syllabus


Useful Links
1 https://archive.nptel.ac.in/courses/124/107/124107008/
2 https://nptel.ac.in/courses/106106138
3 https: //www.coursera.org/specializations/virtual -reality

Page 32

Course Code: Course Title Credit
CSDO7022 Blockchain Technologies 3


Prerequisite: Cryptography and Distributes systems
Course Objectives:
1 To get acquainted with the concept of Distributed ledger system and Blockchain.
2 To learn the concepts of consensus and mining in Blockchain through the Bitcoin network.
3 To understand Ethereum and develop -deploy smart contracts using different tools and
frameworks.
4 To understand permissioned Blockchain and explore Hyperledger Fabric.
5 To understand different types of crypto assets.
Course Outcomes:
1 Describe the basic concept of Blockchain and Distributed Ledger Technology.
2 Interpret the knowledge of the Bitcoin network, nodes, keys, wallets and transactions
3 Implement smart contracts in Ethereum using different development frameworks.
4 Develop applications in permissioned Hyperledger Fabric network.
5 Interpret different Crypto assets and Crypto currencies
6 Analyze the use of Blockchain with AI, IoT and Cyber Security using case studies.

Module Content Hrs
1 Introduction to Blockchain 5
1.1 Distributed Ledger Technologies: Introduction to blockchain: History,
evolution, fundamentals concepts, components, types.
Block in a Blockchain: Structure of a Block, Block Header Hash and
Block Height, The Genesis Block, Linking Blocks in the Blockchain,
Merkle Tree.
2 Consensus Protocol and Bitcoin blockchain 6

Page 33

2.1 Consensus : Byzantine Generals Problem, consensus algorithms: PoW, PoS,
PoET, PoA, LPoS, pBFT, Proof -of-Burn (PoB), Life of a miner, Mining
difficulty, Mining pool and its methods.
2.2 Bitcoin : What is Bitcoin, history of Bitcoin, Bitcoin Common
terminologies: keys, addresses and nodes, Bitcoin mining, hashcash, Block
propagation and relay, bitcoin scripts, transaction in the bitcoin network.
3 Ethereum and Smart Contracts 8
3.1 Ethereum: History, Components, Architecture of Ethereum, Consensus,
Miner and mining node, Ethereum virtual machine, Ether, Gas, Transactions,
Accounts, Patricia Merkle Tree, Swarm, Whisper and IPFS, complete
transaction working and steps in Ethereum, Case study of Ganache for
Ethereum blockchain. Exploring etherscan.io and ether block
structure, Comparison between Bitcoin and Ethereum
3.2 Smart Contracts: history, characteristics, working of smart contracts, types,
Oracles, Structure & Limitations.
Solidity programming: set-up tools and installation, Basics, functions,
Visibility and Activity Qualifiers, Ethereum networks, solidity compiler,
solidity files and structure of contracts, data types, storages, array, functions,
Developing and executing smart contracts in Ethereum. Smart
Contracts Use cases, Opportunities and Risk.
4 Private and Consortium blockchains 9
4.1 Introduction to Private Blockchain: Key characteristics, need, Examples
of Private and Consortium blockchains, Smart contracts in private
blockchain.
4.2 Introduction to Hyperledger, Tools and Frameworks, Hyperledger Fabric,
Comparison between Hyperledger Fabric & Other Technologies.
Hyperledger Platform, Paxos and Raft consensus, Ripple and Corda
blockchains, Byzantine Faults: Byzantine Fault Tolerant (BFT) and
Practical BFT.
5 Cryptocurrencies and digital tokens 6

Page 34

5.1 Cryptocurrency basics, types, usage, ERC20 and ERC721 Tokens,
comparison between ERC20 & ERC721, ICO: basics and related terms,
launching an ICO, pros and cons, evolution and platforms, STO, Different
Crypto currencies, Defi, Metaverse, Types of cryptocurrencies. Bitcoin,
Altcoin, and Tokens (Utility and Security), C ryptocurrency wallets: Hot and
cold wallets, Cryptocurrency usage, Transactions in Blockchain,
UTXO and double spending problem
6 Blockchain applications, Tools and case studies 5
6.1 Applications of Blockchain : Various domains including Education,
Energy, Healthcare, real-estate, logistics, supply chain.
Tools: Corda, Ripple, Quorum and other Emerging Blockchain Platforms,
Case Study on any of the Blockchain Platforms.


Textbooks:
1. Blockchain Technology, Chandramouli Subramanian, Asha A George, Abhillash K. A and
Meena Karthikeyen, Universities press.
2. Solidity Programming Essentials: A beginner's Guide to Build Smart Contracts for Ethereum
and Blockchain, Ritesh Modi, Packt publication
3. Hyperledger Fabric In-Depth: Learn, Build and Deploy Blockchain Applications Using
Hyperledger Fabric, Ashwani Kumar, BPB publications
4. Cryptoassets: The Innovative Investor‘s Guide to Bitcoin and Beyond, Chris Burniske & Jack
Tatar.
5 Mastering Ethereum, Building Smart Contract and Dapps, Andreas M. Antonopoulos Dr.
Gavin Wood, O‘reilly.
References:
1. Mastering Bitcoin, programming the open Blockchainǁ, 2nd Edition by Andreas M.
Antonopoulos, June 2017, Publisher(s): O'Reilly Media, Inc. ISBN: 9781491954386.
2. Mastering Ethereum, Building Smart Contract and Dapps, Andreas M. Antonopoulos Dr. Gavin
Wood, O'reilly.
3. Blockchain Technology: Concepts and Applications, Kumar Saurabh and Ashutosh Saxena,
Wiley Publication.

Page 35

4. The Basics of Bitcoins and Blockchains: An Introduction to Cryptocurrencies and the
Technology that Powers Them, Antony Lewis. for Ethereum and Blockchain, Ritesh Modi,
Packt publication. University of Mumbai, B. E. (Information Technology), Rev 2016 276


Assessment :
Internal Assessment:
Assessment consists of two class tests of 20 marks each. The first class test is to be conducted when
approx. 40% syllabus is completed and second class test when additional 40% syllabus is completed.
Duration of each test shall be one hour.
End Semester Theory Examination:
1 Question paper will comprise of total six questions.
2 All question carries equal marks
3 Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then
part (b) will be from any module other than module 3)
4 Only Four question need to be solved
5 In question paper weightage of each module will be proportional to number of respective lecture
hours as mention in the syllabus


Useful Links
1 NPTEL courses: Blockchain and its Applications, Blockchain Architecture Design and Use
Cases
2 https://ethereum.org/en/
3 https://www .trufflesuite.com/tutorials
4 https://hyperledger -fabric.readthedocs.io/en/release -2.2/
5 Blockchain demo: https://andersbrownworth.com/blockchain/
6 Blockchain Demo: Public / Private Keys & Signing:

Page 36

Course
Code: Course Title Credit
CSDO7023 Game Theory for Data Science
3

Prerequisite: Probability Algebra
Course Objectives :

Sr.No. Course Objectives
1. To introduce the student to the notion of a game, its solutions concepts, and other
basic notions and tools of game theory, and the main applications for which they are
appropriate, including electronic trading markets.
2. To formalize the notion of strategic thinking and rational choice by using the tools
of game theory, and to provide insights into using game theory in modeling
applications.
3. To draw the connections between game theory, computer science, and economics,
especially emphasizing the computational issues.
4. To introduce contemporary topics in the intersection of game theory, computer
science, and economics.
5. To apply game theory in searching, auctioning and trading.

Course Outcomes :

Sr.No. Course Outcomes
On successful completion, of course, learner/student will be able to:
1. Analyze and Discuss the notion of a strategic game and equilibria and identify the
characteristics of main applications of these concepts.
2. Discuss the use of Nash Equilibrium for other problems. Identify key strategic aspects and
based on these be able to connect them to appropriate game theoretic concepts given a real
world situation.
3. Identify some applications that need aspects of Bayesian Games. Implement a typical Virtual
Business scenario using Game theory.
4. Identify and discuss working principle of Non-Cooperative Games
5. Discuss the Mechanism for Design Aggregating Preferences
6. Identify and discuss working principle : Repeated Games

Page 37

DETAILED SYLLABUS:

Sr.
No. Module Detailed Content Hours
0 Prerequisite Probability , Algebra 1
I Introduction: Making rational choices: basics of Games – strategy –
preferences – payoffs – Mathematical basics – Game theory
– Rational Choice – Basic solution
concepts -non-cooperative versus cooperative games – Basic
computational issues – finding equilibria and learning in
gamesTypical application areas for game theory (e.g.
Google’s sponsored search, eBay auctions, electricity
trading markets). 6
II Games with Perfect
Information: Strategic games – prisoner’s dilemma, matching pennies -
Nash equilibria – theory and illustrations – Cournot’s and
Bertrand models of oligopoly – auctions – mixed strategy
equilibrium – zero-sum games – Extensive Games with
Perfect Information – repeated games (prisoner’s dilemma)
– subgame perfect Nash equilibrium; computational issues. 7
III Games with
Imperfect
Information: Games with Imperfect Information – Bayesian Games –
Motivational Examples – General Definitions – Information
aspects – Illustrations – Extensive Games with Imperfect –
Information – Strategies – Nash Equilibrium – Beliefs and
sequential equilibrium – Illustrations – Repeated Games –
The Prisoner’s Dilemma – Bargaining. 6
IV Non-Cooperative
Game Theory: Non-cooperative Game Theory – Self-interested agents –
Games in normal form – Analyzing games: from optimality
to equilibrium – Computing Solution Concepts of Normal –
Form Games – Computing Nash equilibria of two-player,
zero-sum games –Computing Nash equilibria of two-player,
generalsum games – Identifying dominated strategies 7
V Mechanism Design
Aggregating
Preferences: Social Choice – Formal Model – Voting – Existence of
social functions – Ranking systems – Protocols for Strategic
Agents: Mechanism Design – Mechanism design with
unrestricted preferences – Efficient mechanisms – Vickrey
and VCG mechanisms (shortest paths) – Combinatorial
auctions – profit maximization Computational applications 6

Page 38

of mechanism design – applications in Computer Science –
Google’s sponsored search – eBay auctions – K-armed
bandits.
VI Repeated Games Repeated games: The Prisoner’s Dilemma , The main idea ,
Preferences ,Infinitely repeated games, Strategies ,Some
Nash equilibria of the infinitely repeated Prisoner’s
Dilemma , Nash equilibrium payoffs of the infinitely
repeated Prisoner’s Dilemma when the players are patient ,
Subgame perfect equilibria and the one-deviation property 6

Textbooks:
1 An Introduction to Game Theory by Martin J. Osborne
2 M. J. Osborne, An Introduction to Game Theory. Oxford University Press, 2004

References:
1 M. Machler, E. Solan, S. Zamir, Game Theory, Cambridge University Press, 2013.
2 N. Nisan, T. Roughgarden, E. Tardos, and V. V. Vazirani (Editors), Algorithmic Game
Theory. Cambridge University Press, 2007.
3 A.Dixit and S. Skeath, Games of Strategy, Second Edition. W W Norton & Co Inc,
2004.
4 YoavShoham, Kevin Leyton -Brown, Multiagent Systems: Algorithmic,
Game -Theoretic, and Logical Foundations, Cambridge University Press 2008.
5 Zhu Han, DusitNiyato, WalidSaad, TamerBasar and Are Hjorungnes, “Game Theory
in Wireless and Communication Networks”, Cambridge University Press, 2012.
6 Y.Narahari, “Game Theory and Mechanism Design”, IISC Press, World Scientific.

Digital References:
1. https://nptel.ac.in/courses/110104063
2. https://onlinecourses.nptel.ac.in/noc19_ge32/preview

Assessment :
Internal Assessment:
Assessment consists of two class tests of 20 marks each. The first class test is to be conducted
when approx. 40% syllabus is completed and second class test when additional 40% syllabus is
completed. Duration of each test shall be one hour.

Page 39

End Semester Theory Examination:
1 Question paper will comprise of total six questions.
2 All question carries equal marks
3 Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3
then part (b) will be from any module other than module 3)
4 Only Four question need to be solved.
5 In question paper weightage of each module will be proportional to number of respective
lecture hours as mention in the syllabus.

Page 40

Course Code Course Name Credits
ILO7011 Product Life Cycle Management 03


Course Objectives: Students will try:
1. To familiarize the students with the need, benefits and components of PLM
2. To acquaint students with Product Data Management & PLM strategies
3. To give insights into new product development program and guidelines for designing and
developing a product
4. To familiarize the students with Virtual Product Development

Course Outcomes: Students will be able to:
1. Gain knowledge about phases of PLM, PLM strategies and methodology for PLM feasibility
study and PDM implementation.
2. Illustrate various approaches and techniques for designing and developing products.
3. Apply product engineering guidelines / thumb rules in designing products for moulding,
machining, sheet metal working etc.
4. Acquire knowledge in applying virtual product development tools for components, machining
and manufacturing plant



Module
Detailed Contents
Hrs




01 Introduction to Product Lifecycle Management (PLM): Product Lifecycle
Management (PLM), Need for PLM, Product Lifecycle Phases, Opportunities of
Globalization, Pre -PLM Environment, PLM Paradigm, Importance & Benefits of PLM,
Widespread Impact of PLM, Focus and Application, A PLM P roject, Starting the PLM
Initiative, PLM Applications
PLM Strategies: Industrial strategies, Strategy elements, its identification, selection
andimplementation, Developing PLM Vision and PLM Strategy ,
Change management for PLM 10





02 Product Design: Product Design and Development Process, Engineering Design,
Organization and Decomposition in Product Design, Typologies of Design Process
Models, Reference Model, Product Design in the Context of the Product Development
Process, Relation with the Development Process Planning Phase, Relation with the Post
design Planning Phase, Methodological Evolution in Product Design, Concurrent
Engineering, Characteristic Features of Concurrent Engineering, Concurrent Engineering
and Life Cycle Approach, New Product Development (NPD) and Strategies, Product
Configuration and Variant Management, The Design for X System, Objective Properties
and Design for X
Tools, Choice of Design for X Tools and Their Use in the Design Process 09

03 Product Data Management (PDM): Product and Product Data, PDM systems and
importance, Components of PDM, Reason for implementing a PDM system,
financial justification of PDM, barriers to PDM implementation 05

Page 41

04 Virtual Product Development Tools: For components, machines, and
manufacturing plants, 3D CAD systems and realistic rendering techniques, 05
Digital mock -up, Model building, Model analysis, Modeling and simulations in Product
Design, Examples/Case studies


05 Integration of Environmental Aspects in Product Design: Sustainable
Development, Design for Environment,Need for Life Cycle Environmental Strategies,
Useful Life Extension Strategies, End-of-Life Strategies, Introduction of Environmental
Strategies into the Design Process, Life Cycle Environmental Strategies and
Considerations for Product Design 05


06 Life Cycle Assessment and Life Cycle Cost Analysis: Properties, and
Framework of Life Cycle Assessment, Phases of LCA in ISO Standards, Fields of
Application and Limitations of Life Cycle Assessment, Cost Analysis and the Life Cycle
Approach, General Framework for LCCA, Evolution of Models for Product Life Cycle
Cost Analysis 05


Assessment :

Internal:
Assessment consists of two tests out of which; one should be compulsory class test and the other is either a
class test or assignment on live problems or course project.

End Semester Theory Examination:
Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question papers
of end semester examination. In question paper weightage of each module will be proportional to number of
respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then part (b)
will be from any module other than module 3)
4. Only Four question need to be solved.


REFERENCES:

1. John Stark, “Product Lifecycle Management: Paradigm for 21st Century Product Realisation”, Springer -
Verlag, 2004. ISBN: 1852338105
2. Fabio Giudice, Guido La Rosa, AntoninoRisitano, “Product Design for the environment -A life cycle
approach”, Taylor & Francis 2006, ISBN: 0849327229
3. SaaksvuoriAntti, ImmonenAnselmie, “Product Life Cycle Management”, Springer, Dreamtech, ISBN:
3540257314
4. Michael Grieve, “Product Lifecycle Management: Driving the next generation of lean thinking”, Tata
McGraw Hill, 2006, ISBN: 0070636265

Page 42

Course Code Course Name Credits
ILO7012 Reliability Engineering 03


Objectives:
1. To familiarize the students with various aspects of probability theory
2. To acquaint the students with reliability and its concepts
3. To introduce the students to methods of estimating the system reliability of simple and complex systems
4. To understand the various aspects of Maintainability, Availability and FMEA procedure

Outcomes: Learner will be able to…
1. Understand and apply the concept of Probability to engineering problems
2. Apply various reliability concepts to calculate different reliability parameters
3. Estimate the system reliability of simple and complexsystems
4. Carry out a Failure Mode Effect and Criticality Analysis



Module
Detailed Contents
Hrs


01 Probability theory: Probability: Standard definitions and concepts; Conditional
Probability, Baye’s Theorem.
Probability Distributions: Central tendency and Dispersion; Binomial, Normal,
Poisson, Weibull, Exponential, relations between them and their significance.
Measures of Dispersion: Mean, Median, Mode, Range, Mean Deviation, Standard
Deviation, Variance, Skewness and Kurtosis.

08



02 Reliability Concepts: Reliability definitions, Importance of Reliability, Quality
Assurance and Reliability, Bath Tub Curve.
Failure Data Analysis: Hazard rate, failure density, Failure Rate, Mean Time To
Failure (MTTF), MTBF, Reliability Functions.
Reliability Hazard Models: Constant Failure Rate, Linearly increasing, Time
Dependent Failure Rate, Weibull Model. Distribution functions and reliability analysis.

08
03 System Reliability: System Configurations: Series, parallel, mixe d
configuration, k out of n structure, Complex systems. 05

04 Reliability Improvement: Redundancy Techniques: Element redundancy, Unit
redundancy, Standby redundancies. Markov analysis.
System Reliability Analysis – Enumeration method, Cut -set method, Success Path
method , Decomposition method.
08


05 Maintainability and Availability: System downtime, Design for Maintainability:
Maintenance requirements, Design methods: Fault Isolation and self -diagnostics, Parts
standardization and Interchangeability, Modularization and Accessibility, Repair Vs
Replacement.
Availability – qualitative aspects.
05

06 Failure Mode, Effects and Criticality Analysis: Failure mode effects analysis,
severity/criticality analysis, FMECA examples. Fault tree construction, basic symbols,
development of functional reliability block diagram, Fau1t tree
analysis and Event tree Analysis
05

Page 43

Assessment
Internal:
Assessment consists of two tests out of which; one should be compulsory class test and the other is either a
class test or assignment on live problems or course project.

End Semester Theory Examination:
Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question papers
of end semester examination. In question paper weightage of each module will be proportional to number of
respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carries equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then part (b)
will be from any module other than module 3)
4. Only Four question need to be solved.


REFERENCES:

1. L.S. Srinath, “Reliability Engineering”, Affiliated East-Wast Press (P) Ltd., 1985.
2. Charles E. Ebeling, “Reliability and Maintainability Engineering”, Tata McGraw Hill.
3. B.S. Dhillion, C. Singh, “Engineering Reliability”, John Wiley & Sons, 1980.
4. P.D.T. Conor, “Practical Reliability Engg.”, John Wiley & Sons, 1985.
5. K.C. Kapur, L.R. Lamberson, “Reliability in Engineering Design”, John Wiley & Sons.
6. Murray R. Spiegel, “Probability and Statistics”, Tata McGraw -Hill Publishing Co. Ltd.

Page 44

Course Code Course Name Credits
ILO7013 Management Information System 03


Objectives:
1. The course is blend of Management and Technical field.
2. Discuss the roles played by information technology in today’s business and define various
technology architectures on which information systems are built
3. Define and analyze typical functional information systems and identify how they meet the needs
of the firm to deliver efficiency and competitive advantage
4. Identify the basic steps in systems development

Outcomes: Learner will be able to…
1. Explain how information systems Transform Business
2. Identify the impact information systems have on an organization
3. Describe IT infrastructure and its components and its current trends
4. Understand the principal tools and technologies for accessing information from databases to improve
business performance and decision making
5. Identify the types of systems used for enterprise -wide knowledge management and how they provide
value for businesses



Module
Detailed Contents
Hrs

01 Introduction To Information Systems (IS): Computer Based Information Systems,
Impact of IT on organizations, Imporance of IS to Society.
Organizational Strategy, Competitive Advantages and IS.
4

02 Data and Knowledge Management: Database Approach, Big Data, Data warehouse and
Data Marts, Knowledge Management.
Business intelligence (BI): Managers and Decision Making,
BI for Data analysis and Presenting Results
7
03 Ethical issues and Privacy: Information Security. Threat to IS, and Security Controls 7

04 Social Computing (SC): Web 2.0 and 3.0, SC in business -shopping, Marketing,
Operational and Analytic CRM,
E-business and E-commerce – B2B B2C.Mobile commerce.
7
05 Computer Networks Wired and Wireless technology, Pervasive computing, Cloud
computing model. 6

06 Information System within Organization: Transaction Processing Systems, Functional
Area Information System, ERP and ERP support of Business Process. Acquiring
Information Systems and Applications: Various System development
life cycle models.
8


Assessment :

Internal:
Assessment consists of two tests out of which; one should be compulsory class test and the other is either a
class test or assignment on live problems or course project.

Page 45

End Semester Theory Examination:
Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question papers
of end semester examination. In question paper weightage of each module will be proportional to number of
respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then part (b)
will be from any module other than module 3)
4. Only Four question need to be solved.


REFERENCES:

1. Kelly Rainer, Brad Prince,Management Information Systems, Wiley
2. K.C. Laudon and J.P. Laudon, Management Information Systems: Managing the Digital Firm, 10th
Ed., Prentice Hall, 2007.
3. D. Boddy, A. Boonstra, Managing Information Systems: Strategy and Organization, Prentice Hall,
2008

Page 46

Course Code Course Name Credits
ILO7014 Design of Experiments 03


Objectives:
1. To understand the issues and principles of Design of Experiments (DOE)
2. To list the guidelines for designing experiments
3. To become familiar with methodologies that can be used in conjunction with experimental designs for
robustness and optimization

Outcomes: Learner will be able to…
1. Plan data collection, to turn data into information and to make decisions that lead to appropriate action
2. Apply the methods taught to real life situations
3. Plan, analyze, and interpret the results of experiments



Module
Detailed Contents
Hrs


01 Introduction
Strategy of Experimentation
Typical Applications of Experimental Design
Guidelines for Designing Experiments
Response Surface Methodology

06




02 Fitting Regression Models
Linear Regression Models
Estimation of the Parameters in Linear Regression Models
Hypothesis Testing in Multiple Regression
Confidence Intervals in Multiple Regression
Prediction of new response observation
Regression model diagnostics
Testing for lack of fit


08




03 Two -Level Factorial Designs
The 22 Design
The 23 Design
The General2k Design
A Single Replicate of the 2k Design
The Addition of Center Points to the 2k Design,
Blocking in the 2k Factorial Design
Split -Plot Designs


07



04 Two -Level Fractional Factorial Designs
The One-Half Fraction of the 2k Design
The One-Quarter Fraction of the 2k Design
The General 2k-p Fractional Factorial Design
Resolution III Designs
Resolution IV and V Designs
Fractional Factorial Split -Plot Designs


07

Page 47

05 Response Surface Methods and Designs
Introduction to Response Surface Methodology
The Method of Steepest Ascent
Analysis of a Second -Order Response Surface
Experimental Designs for Fitting Response Surfaces

07

06 Taguchi Approach
Crossed Array Designs and Signal -to-Noise Ratios
Analysis Methods
Robust design examples
04


Assessment :

Internal:
Assessment consists of two tests out of which; one should be compulsory class test and the other is either a
class test or assignment on live problems or course project.

End Semester Theory Examination:
Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question papers
of end semester examination. In question paper weightage of each module will be proportional to number of
respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then part (b)
will be from any module other than module 3)
4. Only Four question need to be solved.

REFERENCES:

1. Raymond H. Mayers, Douglas C. Montgomery, Christine M. Anderson -Cook, Response Surface
Methodology: Process and Product Optimization using Designed Experiment, 3rd edition,John
Wiley & Sons, New York, 2001
2. D.C. Montgomery, Design and Analysis of Experiments, 5th edition, John Wiley &Sons, New York,
2001
3. George E P Box, J Stuart Hunter, William G Hunter, Statics for Experimenters: Design,
Innovation and Discovery, 2nd Ed. Wiley
4. W J Dimond, Peactical Experiment Designs for Engineers and Scintists, John Wiley and Sons Inc.
ISBN: 0-471-39054 -2
5. Design and Analysis of Experiments (Springer text in Statistics), Springer by A.M. Dean,and
D. T.Voss

Page 48

Course Code Course Name Credits
ILO7015 Operations Research 03


Objectives:
1. Formulate a real-world problem as a mathematical programming model.
2. Understand the mathematical tools that are needed to solve optimization problems.
3. Use mathematical software to solve the proposed models.

Outcomes: Learner will be able to…
1. Understand the theoretical workings of the simplex method, the relationship between a linear program
and its dual, including strong duality and complementary slackness.
2. Perform sensitivity analysis to determine the direction and magnitude of change of a model’s optimal
solution as the data change.
3. Solve specialized linear programming problems like the transportation and assignment problems, solve
network models like the shortest path, minimum spanning tree, and maximum flow problems.
4. Understand the applications of integer programming and a queuing model and compute important
performance measures



Module
Detailed Contents
Hrs










01 Introduction to Operations Research : Introduction, , Structure of the
Mathematical Model, Limitations of Operations Research
Linear Programming : Introduction, Linear Programming Problem,
Requirements of LPP, Mathematical Formulation of LPP, Graphical method,
Simplex Method Penalty Cost Method or Big M -method, Two Phase Method, Revised
simplex method, Duality , Primal – Dual construction, Symmetric and Asymmetric
Dual, Weak Duality Theorem, Complimentary Slackness Theorem, Main Duality
Theorem, Dual Simplex Method, Sensitivity Analysis Transportation Problem :
Formulation, solution, unbalanced Transportation problem. Finding basic feasible
solutions – Northwest corner rule, least cost method and Vogel’s approximation
method. Optimality test: the stepping stone method and MODI method.
Assignment Problem : Introduction, Mathematical Formulation of the Problem,
Hungarian Method Algorithm, Processing of n Jobs Through Two Machines and m
Machines, Graphical Method of Two Jobs m Machines Problem Routing Problem,
Travelling Salesman Problem
Integer Programming Problem : Introduction, Types of Integer Programming Problems,
Gomory’s cutting plane Algorithm, Branch and Bound Technique.
Introduction to Decomposition algorithms.









14

02 Queuing models : queuing systems and structures, single server and multi -server
models, Poisson input, exponential service, constant rate service, finite and infinite
population
05
03 Simulation : Introduction, Methodology of Simulation, Basic Concepts, 05


220

Page 49

Simulation Procedure, Application of Simulation Monte -Carlo Method:
Introduction, Monte -Carlo Simulation, Applications of Simulation, Advantages of
Simulation, Limitations of Simulation

04 Dynamic programming . Characteristics of dynamic programming. Dynamic
programming approach for Priority Management employment smoothening,
capital budgeting, Stage Coach/Shortest Path, cargo loading and Reliability problems.
05

05 Game Theory . Competitive games, rectangular game, saddle point, minimax
(maximin) method of optimal strategies, value of the game. Solution of games with saddle
points, dominance principle. Rectangular games without saddle point – mixed strategy
for 2 X 2 games.
05
06 Inventory Models : Classical EOQ Models, EOQ Model with Price Breaks,
EOQ with Shortage, Probabilistic EOQ Model, 05

Assessment :

Internal:
Assessment consists of two tests out of which; one should be compulsory class test and the other is either a
class test or assignment on live problems or course project.

End Semester Theory Examination:
Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question papers
of end semester examination. In question paper weigh tage of each module will be proportional to number of
respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then part (b)
will be from any module other than module 3)
4. Only Four question need to be solved.

REFERENCES:

1. Taha, H.A. "Operations Research - An Introduction", Prentice Hall, (7th Edition), 2002.
2. Ravindran, A, Phillips, D. T and Solberg, J. J. "Operations Research: Principles and Practice", John
Willey and Sons, 2nd Edition, 2009.
3. Hiller, F. S. and Liebermann, G. J. "Introduction to Operations Research", Tata McGraw Hill, 2002.
4. Operations Research, S. D. Sharma, KedarNath Ram Nath -Meerut.
5. Operations Research, KantiSwarup, P. K. Gupta and Man Mohan, Sultan Chand & Sons.

Page 50

Course Code Course Name Credits
ILO7016 Cyber Security and Laws 03


Objectives:
1. To understand and identify different types cybercrime and cyber law
2. To recognized Indian IT Act 2008 and its latest amendments
3. To learn various types of security standards compliances

Outcomes: Learner will be able to…
1. Understand the concept of cybercrime and its effect on outside world
2. Interpret and apply IT law in various legal issues
3. Distinguish different aspects of cyber law
4. Apply Information Security Standards compliance during software design and development



Module
Detailed Contents
Hrs

01 Introduction to Cybercrime: Cybercrime definition and origins of the world,
Cybercrime and information security, Classifications of cybercrime, Cybercrime and the
Indian ITA 2000, A global Perspective on cybercrimes.
4




02 Cyber offenses & Cybercrime: How criminal plan the attacks, Social Engg, Cyber
stalking, Cyber café and Cybercrimes, Bot nets, Attack vector, Cloud computing,
Proliferation of Mobile and Wireless Devices, Trends in Mobility, Credit Card Frauds in
Mobile and Wireless Computing Era, Security Challenges Posed by Mobile Devices,
Registry Settings for Mobile Devices, Authentication Service Security, Attacks on
Mobile/Cell Phones, Mobile Devices: Security Implications for Organizations,
Organizational Measures for Handling Mobile, Devices -Related Security Issues,
Organizational Security Policies and Measures in Mobile
Computing Era, Laptops



9

03 Tools and Methods Used in Cyber line
Phishing, Password Cracking, Key loggers and Spywares, Virus and Worms,
Steganography, DoS and DDoS Attacks, SQL Injection, Buffer Over Flow, Attacks on
Wireless Networks, Phishing, Identity Theft (ID Theft)
6



04 The Concept of Cyberspace
E-Commerce , The Contract Aspects in Cyber Law ,The Security Aspect of Cyber
Law ,The Intellectual Property Aspect in Cyber Law, The Evidence Aspect in
Cyber Law , The Criminal Aspect in Cyber Law, Global Trends in Cyber Law ,
Legal Framework for Electronic Data
Interchange Law Relating to Electronic Banking , The Need for an Indian Cyber
Law


8

05 Indian IT Act.
Cyber Crime and Criminal Justice : Penalties, Adjudication and Appeals Under the IT
Act, 2000, IT Act. 2008 and its Amendments
6
06 Information Security Standard compliances
SOX, GLBA, HIPAA, ISO, FISMA, NERC, PCI. 6

Page 51

Assessment :

Internal:
Assessment consists of two tests out of which; one should be compulsory class test and the other is either a
class test or assignment on live problems or course project.

End Semester Theory Examination:
Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question
papers of end semester examination.

In question paper weightage of each module will be proportional to number of respective lecture hours as
mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then part (b)
will be from any module other than module 3)
4. Only Four question need to be solved.


REFERENCES:

1. Nina Godbole, Sunit Belapure, Cyber Security , Wiley India, New Delhi
2. The Indian Cyber Law by Suresh T. Vishwanathan; Bharat Law House New Delhi
3. The Information technology Act, 2000; Bare Act- Professional Book Publishers, New Delhi.
4. Cyber Law & Cyber Crimes By Advocate Prashant Mali; Snow White Publications, Mumbai
5. Nina Godbole, Information Systems Security, Wiley India, New Delhi
6. Kennetch J. Knapp, Cyber Security &Global Information Assurance Information Science Publishing.
7. William Stallings , Cryptography and Network Security, Pearson Publication
8. Websites for more information is available on : The Information Technology ACT, 2008 -
TIFR : https://www.tifrh.res.in
9. Website for more information , A Compliance Primer for IT professional :
https:/ /www.sans.org/reading -room/whitepapers/compliance/compliance -primer -professionals - 33538

Page 52

Course Code Course Name Credits
ILO7017 Disaster Management and Mitigation Measures 03

Objectives:
1. To understand physics and various types of disaster occurring around the world
2. To identify extent and damaging capacity of a disaster
3. To study and understand the means of losses and methods to overcome /minimize it.
4. To understand role of individual and various organization during and after disaster
5. To understand application of GIS in the field of disaster management
6. To understand the emergency government response structures before, during and after
disaster

Outcomes: Learner will be able to…
1. Get to know natural as well as manmade disaster and their extent and possible effects on the
economy.
2. Plan of national importance structures based upon the previous history.
3. Get acquainted with government policies, acts and various organizational structure
associated with an emergency.
4. Get to know the simple do’s and don’ts in such extreme events and act accordingly.


Module
Detailed Contents
Hrs


01 Introduction
1.1 Definition of Disaster, hazard, global and Indian scenario, general perspective,
importance of study in human life, Direct and indirect effects of disasters, long term
effects of disasters. Introduction to global warming and
climate change.

03




02 Natural Disaster and Manmade disasters:
Natural Disaster: Meaning and nature of natural disaster, Flood, Flash flood, drought,
cloud burst, Earthquake, Landslides, Avalanches, Volcanic eruptions, Mudflow,
Cyclone, Storm, Storm Surge, climate change, global warming, sea level rise, ozone
depletion
Manmade Disasters: Chemical, Industrial, Nuclear and Fire Hazards. Role of growing
population and subsequent industrialization, urbanization and changing lifestyle of
human beings in frequent occurrences of manmade
disasters.



09




03 Disaster Management, Policy and Administration
Disaster management: meaning, concept, importance, objective of disaster
management policy, disaster risks in India, Paradigm shift in disaster management.
Policy and administration:
Importance and principles of disaster management policies, command and co -
ordination of in disaster management, rescue operations -how to start with
and how to proceed in due course of time, study of flowchart showing the entire
process.



06


04 Institutional Framework for Disaster Management in India:
4.1 Importance of public awareness, Preparation and execution of emergency management
programme.Scope and responsibilities of National Institute of Disaster Management
(NIDM) and National disaster management authority
(NDMA) in India.Methods and measures to avoid disasters, Management of

06

Page 53

casualties, set up of emergency facilities, importance of effective
communication amongst different agencies in such situations.
4.2 Use of Internet and softwares for effective disaster management.
Applications of GIS, Remote sensing and GPS in this regard.



05 Financing Relief Measures:
Ways to raise finance for relief expenditure, role of government agencies and NGO’s in
this process, Legal aspects related to finance raising as well as overall management of
disasters. Various NGO’s and the works they have carried out in the past on the occurrence
of various disasters, Ways to approach these teams.
International relief aid agencies and their role in extreme events.


09




06 Preventive and Mitigation Measures:
Pre-disaster, during disaster and post -disaster measures in some events in general
Structural mapping: Risk mapping, assessment and analysis, sea walls and embankments,
Bio shield, shelters, early warning and communication
Non Structural Mitigation: Community based disaster preparedness, risk transfer and
risk financing, capacity development and training, awareness and education, contingency
plans.
Do’s and don’ts in case of disasters and effective implementation of relief
aids.



06

Assessment :

Internal:
Assessment consists of two tests out of which; one should be compulsory class test and the other is either a
class test or assignment on live problems or course project.

End Semester Theory Examination:
Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question papers
of end semester examination. In question paper weightage of each module will be proportional to number of
respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then part (b)
will be from any module other than module 3)
4. Only Four question need to be solved.

REFERENCES:

1. ‘Disaster Management’ by Harsh K.Gupta, Universities Press Publications.
2. ‘Disaster Management: An Appraisal of Institutional Mechanisms in India’ by O.S.Dagur, published by
Centre for land warfare studies, New Delhi, 2011.
3. ‘Introduction to International Disaster Management’ by Damon Copolla, Butterworth Heinemann
Elseveir Publications.
4. ‘Disaster Management Handbook’ by Jack Pinkowski, CRC Press Taylor and Francis group.
5. ‘Disaster management & rehabilitation’ by Rajdeep Dasgupta, Mittal Publications, New Delhi.
6. ‘Natural Hazards and Disaster Management, Vulnerability and Mitigation – R B Singh, Rawat
Publications
7. Concepts and Techniques of GIS –C.P.Lo Albert, K.W. Yonng – Prentice Hall (India) Publications.
(Learners are expected to refer reports published at national and International level and updated
information availab le on authentic web sites)

Page 54

Course Code Course Name Credits
ILO7018 Energy Audit and Management 03


Objectives:

1. To understand the importance energy security for sustainable development and the
fundamentals of energy conservation.
2. To introduce performance evaluation criteria of various electrical and thermal installations to
facilitate the energy management
3. To relate the data collected during performance evaluation of systems for identification of energy
saving opportunities.

Outcomes: Learner will be able to…

1. To identify and describe present state of energy security and its importance.
2. To identify and describe the basic principles and methodologies adopted in energy audit of an utility.
3. To describe the energy performance evaluation ofsome common electrical installations and identify
the energy saving opportunities.
4. To describe the energy performance evaluation ofsome common thermal installations and identify
the energy saving opportunities
5. To analyze the data collected during performance evaluation and recommend energy saving
measures


Module
Detailed Contents
Hrs


01 Energy Scenario:
Present Energy Scenario, Energy Pricing, Energy Sector Reforms, Energy Security,
Energy Conservation and its Importance, Energy Conservation Act- 2001 and its Features.
Basics of Energy and its various forms, Material and
Energy balance

04




02 Energy Audit Principles:
Definition, Energy audit - need, Types of energy audit, Energy management (audit)
approach -understanding energy costs, Bench marking, Energy performance, Matching
energy use to requirement, Maximizing system efficiencies, Optimizing the input energy
requirements, Fuel and energy substitution. Elements of monitoring& targeting; Energy
audit Instruments; Data and information -analysis.
Financial analysis techniques: Simple payback period, NPV, Return on investment (ROI),
Internal rate of return (IRR)



08



03 Energy Management and Energy Conservation in Electrical System: Electricity
billing, Electrical load management and maximum demand Control; Power factor
improvement, Energy efficient equipments and appliances, star ratings.
Energy efficiency measures in lighting system, Lighting control: Occupancy
sensors, daylight integration, and use of intelligent controllers.
Energy conservation opportunities in: water pumps, industrial drives, induction motors,
motor retrofitting, soft starters, variable speed drives.


10

Page 55

04 Energy Management and Energy Conservation in Thermal Systems:
Review of different thermal loads; Energy conservation opportunities in: Steam
distribution system, Assessment of steam distribution losses, Steam leakages, Steam
trapping, Condensate and flash steam recovery system.
General fuel economy measures in Boilers and furnaces, Waste heat recovery, use of
insulation - types and application. HVAC system: Coeffici ent of performance, Capacity,
factors affecting Refrigeration and Air Conditioning system performance and savings
opportunities.


10

05 Energy Performance Assessment:
On site Performance evaluation techniques, Case studies based on: Motors and variable
speed drive, pumps, HVAC system calculations; Lighting System:
Installed Load Efficacy Ratio (ILER) method, Financial Analysis.
04

06 Energy conservation in Buildings:
Energy Conservation Building Codes (ECBC): Green Building, LEED rating,
Application of Non-Conventional and Renewable Energy Sources
03


Assessment :

Internal:
Assessment consists of two tests out of which; one should be compulsory class test and the other is either a
class test or assignment on live problems or course project.

End Semester Theory Examination:
Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question papers
of end semester examination. In question paper weightage of each module will be proportional to number of
respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then part (b)
will be from any module other than module 3)
4. Only Four question need to be solved.

REFERENCES:

1. Handbook of Electrical Installation Practice, Geofry Stokes, Blackwell Science
2. Designing with light: Lighting Handbook, By Anil Valia, Lighting System
3. Energy Management Handbook, By W.C. Turner, John Wiley and Sons
4. Handbook on Energy Audits and Management, edited by A. K. Tyagi, Tata Energy
Research Institute (TERI).
5. Energy Management Principles, C.B.Smith, Pergamon Press
6. Energy Conservation Guidebook, Dale R. Patrick, S. Fardo, Ray E. Richardson, Fairmont Press
7. Handbook of Energy Audits, Albert Thumann, W. J. Younger, T. Niehus, CRC Press
8. www.energymanagertraining.com
9. www.bee -india.nic.in

Page 56

Course Code Course Name Credits
ILO7019 Development Engineering 03

Objectives:

1. To familiarise the characteristics of rural Society and the Scope, Nature and Constraints of rural
Development
2. To provide an exposure toimplications of 73rdCAA on Planning, Development and Governance of Rural
Areas
3. An exploration of human values, which go into making a ‘good’ human being, a ‘good’ professional, a
‘good’ society and a ‘good life’ in the context of work life and the personal life of modern Indian
professionals
4. To familiarise the Nature and Type of Human Values relevant to Planning Institutions

Outcomes: Learner will be able to…

1. Demonstrateunderstanding of knowledge for Rural Development.
2. Prepare solutions for Management Issues.
3. Take up Initiatives and design Strategies to complete the task
4. Develop acumen for higher education and research.
5. Demonstrate the art of working in group of different nature
6. Develop confidence to take up rural project activities independently



Module Contents Hrs
1 Introduction to Rural Development Meaning, nature and scope of development; Nature of
rural society in India; Hierarchy of settlements; Social, economic and ecological constraints
for rural development
Roots of Rural Development in India Rural reconstructi on and Sarvodaya programme
before independence; Impact of voluntary effort and Sarvodaya Movement on rural
development; Constitutional direction, directive principles; Panchayati Raj - beginning of
planning and community development; National extension services. 08
2 Post-Independence rural Development Balwant Rai Mehta Committee - three tier system of
rural local Government; Need and scope for people’s participation and Panchayati Raj;
Ashok Mehta Committee - linkage between Panchayati Raj, participation and rural
development. 06
3 Rural Development Initiatives in Five Year Plans Five Year Plans and Rural Development;
Planning process at National, State, Regional and District levels; Planning, development,
implementing and monitoring organizations and agencies; Urban and rural interface -
integrated approach and local plans; Development initiatives and their convergence; Special
component plan and sub -plan for the weaker section; Micro -eco zones; Data base f or local
planning; Need for decentralized planning; Sustainable rural development 07

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4 Post 73rd Amendment Scenario 73rd Constitution Amendment Act, including - XI
schedule, devolution of powers, functions and finance; Panchayati Raj institutions
- organizational linkages; Recent changes in rural local planning; Gram Sabha - revitalized
Panchayati Raj; Institutionalization; resource mapping, resource mobilization including
social mobilization; Information Technology and rural planning; Need for further
amendments. 04
5 Values and Science and Technology Material development and its values; the
challenge of science and technology; Values in planning profession, research and education

Types of Values Psychological values — integrated personality; mental health; Societal
values — the modern search for a good society; justice, democracy, rule of law, values in
the Indian constitution; Aesthetic values — perception and enjoyment of beauty; Moral and
ethical values; nature of moral judgment; Spiritual values; different concepts; secular
spirituality; Relative and absolute values; Human values — humanism and human values;
human rights; human values as freedom, creativity, love and wisdom 10
6 Ethics Canons of ethics; ethics of virtue; ethics of duty; ethics of responsibility;
Work ethics; Professional ethics; Ethics in planning profession, research and education 04

Assessment :

Internal Assessment for 20 marks:
Consisting Two Compulsory Class Tests
First test based on approximately 40% of contents and second test based on remaining contents
(approximately 40% but excluding contents covered in Test I)

End Semester Examination:
Weightage of each module in end semester examination will be proportional to number of respective lecture
hours mentioned in the curriculum.
1. Question paper will comprise of total six questions , each carrying 20 marks
2. Question 1 will be compulsory and should cover maximum contents of the curriculum
3. Remaining questions will be mixed in nature (for example if Q.2 has part (a) from module 3 then part
(b) will be from any module other than module 3)
4. Only Four questions need to be solved

Reference
1. ITPI, Village Planning and Rural Development, ITPI, New Delhi
2. Thooyavan, K.R. Human Settlements: A 2005 MA Publication, Chennai
3. GoI, Constitution (73rdGoI, New Delhi Amendment) Act, GoI, New Delhi
4. Planning Commission, Five Year Plans, Planning Commission
5. Planning Commission, Manual of Integrated District Planning, 2006, Planning Commission New Delhi
6. Planning Guide to Beginners
7. Weaver, R.C., The Urban Complex, Doubleday
8. Farmer, W.P. et al, Ethics in Planning, American Planning Association, Washington

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9. How, E., Normative Ethics in Planning, Journal of Planning Literature, Vol.5, No.2, pp. 123-150
10. Watson, V. Conflicting Rationalities: -- Implications for Planning Theory and Ethics, Planning Theory
and Practice, Vol. 4, No.4, pp.395 – 407

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Lab Code Lab Name Credit
CSL701 Deep Learning Lab 1


Prerequisite: Python Programming, Engineering Mathematics

Lab Objectives:
1 To implement basic neural network models.
2 To implement various training algorithms for feedforward neural networks.
3 To design deep learning models for supervised, unsupervised and sequence learning.

Lab Outcomes: At the end of the course, the students will be able to
1 Implement basic neural network models.
2 Design and train feedforward neural networks using various learning algorithms and
optimize model performance.
3 Build and train deep learning models such as Autoencoders, CNNs, RNN, LSTM,GRU etc.



Suggested List of Experiments
1. Based on Module 1 using Virtual Lab
1. Implement Multilayer Perceptron algorithm to simulate XOR gate.
2. To explore python libraries for deep learning e.g. Theano, TensorFlow etc.
2 Module 2 (Any Two)
3. Apply any of the following learning algorithms to learn the parameters of the
supervised single layer feed forward neural network.
a. Stochastic Gradient Descent
b. Mini Batch Gradient Descent
c. Momentum GD
d. Nestorev GD
e. Adagrad GD
f. Adam Learning GD
4. Implement a backpropagation algorithm to train a DNN with at least 2 hidden layers.
5.Design and implement a fully connected deep neural network with at least 2 hidden
layers for a classification application. Use appropriate Learning Algorithm, output
function and loss function.
3. Module 3 (Any One)

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6. Design the architecture and implement the autoencoder model for Image
Compression.
7. Design the architecture and implement the autoencoder model for Image
denoising.
4 Module 4 (Any One)
8. Design and implement a CNN model for digit recognition application.
9. Design and implement a CNN model for image classification.
Module 5 (Any Two)
10. Design and implement LSTM model for handwriting recognition, speech
recognition, machine translation, speech activity detection, robot control, video
games, time series forecasting etc.
11. Design and implement GRU for any real life applications, chat bots etc.
12. Design and implement RNN for classification of temporal data , sequence to
sequence data modelling etc.



Textbooks:
1 Ian Goodfellow, Yoshua Bengio, Aaron Courville. ―Deep Learning , MIT Press Ltd,
2016
2 Li Deng and Dong Yu, ―Deep Learning Methods and Applications , Publishers Inc.
3 Satish Kumar "Neural Networks A Classroom Approach" Tata McGraw -Hill.
4 JM Zurada ―Introduction to Artificial Neural Systemsǁ, Jaico Publishing House
5 M. J. Kochenderfer, Tim A. Wheeler. ―Algorithms for Optimization , MIT Press.
References:
1 Deep Learning from Scratch: Building with Python from First Principles - Seth Weidman
by O`Reilley
2 François Chollet. ―Deep learning with Python ―(Vol. 361). 2018 New York: Manning.
3 Douwe Osinga. ―Deep Learning Cookbookǁ, O‘REILLY, SPD Publishers, Delhi.
4 Simon Haykin, Neural Network - A Comprehensive Foundation - Prentice Hall
International, Inc
5 S.N.Sivanandam and S.N.Deepa, Principles of soft computing -Wiley India
Web References:
1 https://keras.io/
2 https://stanford.edu/~shervine/teaching/cs -230/cheatsheet -recurrent -neural -networks
3 https://keras.io/examples/vision/autoencoder/
4 https://stanford.edu/~shervine/teaching/cs -230/cheatsheet -convolu tional -neural -networks

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Term Work:
1 Term work should consist of 8 experiments.
2 The final certification and acceptance of term work ensures satisfactory performance of
laboratory work and minimum passing marks in term work.
3 The final certification and acceptance of term work ensures satisfactory performance of
laboratory work and minimum passing marks in term work. Total 25 Marks (Experiments:
15-marks, Attendance Theory & Practical: 05-marks, Assignment: 05-marks)

Practical and Oral exam
Oral examination based on the entire syllabus of CSC:701

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Course Code Course Name Credits
CSL702 Big Data Analytics Lab 1


Prerequisite: Java/Python
Lab Objectives:
1 To provide an overview of an exciting growing field of big data analytics.
2 To introduce programming skills to build simple solutions using big data technologies such as
MapReduce and scripting for NoSQL, and the ability to write parallel algorithms for
multiprocessor execution.
3 To teach the fundamental techniques and principles in achieving big data analytics with
scalability and streaming capability.
4 To enable students to have skills that will help them to solve complex real-world problems in
decision support.
Lab Outcomes:
1 Understand the key issues in big data management and its associated applications for business
decisions and strategy.
2 Develop problem solving and critical thinking skills in fundamental enabling techniques like
Hadoop, Map reduce and NoSQL in big data analytics.
3 Collect, manage, store, query and analyze various forms of Big Data.
4 Interpret business models and scientific computing paradigms, and apply software tools for big
data analytics.
5 Adapt adequate perspectives of big data analytics in various applications like recommender
systems, social media applications etc.
6 Solve Complex real world problems in various applications like recommender systems, social
media applications, health and medical systems, etc.

Suggested Experiments:

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Sr.
No. Name of the Experiment
1 Hadoop HDFS Practical: -HDFS Basics, Hadoop Ecosystem Tools Overview. -Installing
Hadoop. -Copying File to Hadoop. -Copy from Hadoop File system and deleting file.
-Moving and displaying files in HDFS. -Programming exercises on Hadoop.
2 Use of Sqoop tool to transfer data between Hadoop and relational database servers. a.
Sqoop - Installation. b. To execute basic commands of Hadoop eco system component
Sqoop.
3 To install and configure MongoDB/ Cassandra/ HBase/ Hypertable to execute NoSQL
commands.
4 Experiment on Hadoop Map -Reduce / PySpark: -Implementing simple algorithms in
Map-Reduce: Matrix multiplication, Aggregates, Joins, Sorting, Searching, etc.
5 Create HIVE Database and Descriptive analytics -basic statistics, visualization using
Hive/PIG/R.
6 Write a program to implement word count programs using MapReduce.
7 Implementing DGIM algorithm using any Programming Language/ Implement Bloom
Filter using any programming language.
8 Implementing any one Clustering algorithm (K-Means/CURE) using Map-Reduce.
9 Streaming data analysis – use flume for data capture, HIVE/PYSpark for analysis of
twitter data, chat data, weblog analysis etc.
10 Implement PageRank using Map-Reduce.
11 Implement predictive Analytics techniques (regression / time series, etc.) using R/
Scilab/ Tableau/ Rapid miner.

Useful Links
1 https://nptel.ac.in/courses/117/102/117102062/
2 https://epgp.inflibnet.ac.in/Home/ViewSubject?catid=305
3 https://nptel.ac.in/courses/106/106/106106167/

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Term Work:
1 Term work should consist of 10 experiments
2 Journal must include at least 2 assignments based on Theory and Practical’s
3 The final certification and acceptance of term work ensures satisfactory performance of
laboratory work and minimum passing marks in term work.
4 Total 25 Marks (Experiments: 15-marks, Attendance Theory & Practical: 05-marks,
Assignments: 05-marks)
Oral & Practical exam:
Oral examination based on the entire syllabus of CSC702 and CSL702

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Course Code: Course Title Credit
CSDOL7011 Natural Language Processing Lab 1


Prerequisite: Java/Python
Lab Objectives:
1 To understand the key concepts of NLP.
2 To learn various phases of NLP
3 To design and implement various language models and POS tagging techniques
4 To understand various NLP Algorithms
5 To learn NLP applications such as Information Extraction, Sentiment Analysis, Question
answering, Machine translation etc.
6 To design and implement applications based on natural language processing
Lab Outcomes:
1 Apply various text processing techniques
2 Design language model for word level analysis
3 Design, implement and analyze NLP algorithms
4 Realize semantics of English language for text processing
5 To apply NLP techniques to design real world NLP applications such as machine translation,
sentiment analysis, text summarization, information extraction, Question Answering system etc.
6 Implement proper experimental methodology for training and evaluating empirical NLP systems

Suggested Experiments:
Sr. No. Name of the Experiment
1 Study various applications of NLP and Formulate the Problem Statement for Mini
Project based on chosen real world NLP applications:
[Machine Translation, Text Categorization, Text summarization, Chat Bot, Plagiarism,
Spelling & Grammar Checkers, Sentiment / Opinion analysis, Question answering,
Personal Assistant, Tutoring Systems, etc.]

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2 Apply various text preprocessing techniques for any given text: Tokenization and
Filtration & Script Validation
3 Apply various other text preprocessing techniques for any given text: Stop Word
Removal, Lemmatization / Stemming
4 Perform morphological analysis and word generation for any given text
5 Implement N-Gram model for the given text input
6 Study the different POS taggers and Perform POS tagging on the given text
7 Perform chunking by analyzing the importance of selecting proper features for training a
model and size of training
8 Implement Named Entity Recognizer for the given text input
9 Implement Text Similarity Recognizer for the chosen text documents
10 Implement word sense disambiguation using LSTM/GRU
11 Exploratory data analysis of a given text (Word Cloud)
12 Mini Project Report: For any one chosen real world NLP application
13 Implementation and Presentation of Mini Project

Useful Links
1 https://nlp -iiith.vlabs.ac.in/List%20of%20experiments.html
2 https://onlinecourses.nptel.ac.in/noc21_cs102/preview
3 https://onlinecourses.nptel.ac.in/noc20_cs87/preview
4 https://nptel.ac.in/courses/106105158


Term Work:
1 Term work should consist of 08 experiments and mini project
2 Journal must include at least 2 assignments based on Theory and Practical’s

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3 The final certification and acceptance of term work ensures satisfactory performance of
laboratory work and minimum passing marks in term work.
4 Total 25 Marks (Experiments: 15-marks, Attendance Theory & Practical: 05-marks,
Assignments: 05-marks)
Oral & Practical exam:
Oral examination based on the entire syllabus of CSDO701 and CSL703

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Course Code: Course Title Credit
CSDOL7012 AI for Healthcare Lab 1


Prerequisites: Python
Lab Objective
1 To Collect, clean, integrate, and transform healthcare data for a specific disease.
2 To Perform exploratory data analysis on healthcare data.
3 To Develop AI models for medical diagnosis using MRI/X -ray data.
4 To Build AI models for medical prognosis.
5 Extract entities from medical reports using natural language processing.
To Predict disease risk using patient data
Lab Outcomes:
After successful completion of the course, the student will be able to:
1 Understand computational models of AI ,
2 Develop healthcare applications using appropriate computational tools.
3 Apply appropriate models to solve specific healthcare problems.
4 Analyze and justify the performance of specific models as applied to healthcare
problems.
5 Design and implement AI based healthcare applications.


Suggested Experiments:
Sr.
No. Name of the Experiment
1 Collect, Clean, Integrate and Transform Healthcare Data based on specific disease.
2 Perform Exploratory data analysis of Healthcare Data.
3 AI for medical diagnosis based on MRI/X -ray data.
4 AI for medical prognosis .
5 Natural language Entity Extraction from medical reports.
6 Predict disease risk from Patient data.
7 Medical Reviews Analysis from social media data.
8 Explainable AI in healthcare for model interpretation.
9 Mini Project -Design and implement innovative web/mobile based AI application using Healthcare
Data. (this needs to be implemented in group of 3-4 students )
10 Documentation and Presentation of Mini Project.




Textbooks:
1 Arjun Panesar, "Machine Learning and AI for Healthcare”, A Press.

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2 Arvin Agah, "Medical applications of Artificial Systems ", CRC Press

References:
1 Erik R. Ranschaert Sergey Morozov Paul R. Algra, “Artificial Intelligence in medical
Imaging - Opportunities, Applications and Risks”, Springer
2 Sergio Consoli Diego Reforgiato Recupero Milan Petković,“Data Science for Healthcare -
Methodologies and Applications”, Springer
3 Dac-Nhuong Le, Chung Van Le, Jolanda G. Tromp, Gia Nhu Nguyen, “Emerging technologies for
health and medicine”, Wiley.
4 Ton J. Cleophas • Aeilko H. Zwinderman, “Machine Learning in Medicine - Complete
Overview”, Springer
Useful Links
1 https://www.coursera.org/learn/introduction -tensorflow?specialization=tensorflow -in-practice
2 https://www.coursera.org/learn/convolutional -neural -networks -tensorflow?specialization=tensorflo w- in-
practice
3 https://datarade.ai/data -categories/electronic -health -record -ehr-data
4 https://www.cms.gov/Medicare/E -Health/EHealthRecords
5 https://www.coursera.org/learn/tensorflow -sequences -time -series -and-prediction?specialization=te
nsorflow -in-practice

Term Work:
1 Term work should consist of 8 experiments and a Mini Project.
2 The final certification and acceptance of term work ensures satisfactory performance of
laboratory
work and minimum passing marks in term work.
3 Total 25 Marks (Experiments: 10-Marks, Mini Project -10 Marks, Attendance Theory &
Practical: 05-
marks)

Oral examination based on the entire syllabus of CSDO7012

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Course Code: Course Title Credit
CSDL7013 Neural Networks and Fuzzy Systems Lab 1


Prerequisite: C/C++/Java/MATLAB
Lab Objectives:
1 Articulate basic knowledge of fuzzy set theory through programing.
2 To design Associative Memory Networks.
3 To apply Unsupervised learning towards Networks design.
4 To demonstrate Special networks and its applications in soft computing.
5 To implement Hybrid computing systems.
Lab Outcomes: At the end of the course, the students will be able to
1 Implement Fuzzy operations and functions towards Fuzzy -rule creations.
2 Build and training Associative Memory Network.
3 Build Unsupervised learning based networks .
4 Design and implement architecture of Special Networks
5 Implement Neuro -Fuzzy hybrid computing applications.

Suggested Experiments:
Sr. No. Name of the Experiment
1 Demonstrate Union and intersection of two Fuzzy Sets.
2 Demonstrate difference between two Fuzzy Sets.
3 Implement Fuzzy membership functions.
4 Implement Fuzzy Inference system (FIS).
5 Implement any De-fuzzification of membership method.
6 Implement Bidirectional Associative Memory(BAM) Network
7 Implement Radial basis function network.
8 Implement Basic Neural Network learning rules.
9 Implement any Unsupervised Learning algorithm.

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10 Implement Kohonen Self- Organizing Feature Maps
11 Implement a Probabilistic Neural Network.
12 Implement any Ensemble neural model.
13 Design any one Neuro -Fuzzy system.

Useful Links
1 https://onlinecourses.nptel.ac.in/noc21_ge07/preview
2 http://www.nitttrc.edu.in/nptel/courses/video/127105006/L25.html
3 https://archive.nptel.ac.in/courses/108/104/108104157/


Term Work:
1 Term work should consist of 08 experiments, 1 case study.
2 Journal must include at least 2 assignments based on Theory and Practical’s.
3 The final certification and acceptance of term work ensures satisfactory performance of
laboratory work and minimum passing marks in term work.
4 Total 25 Marks (Experiments: 15-marks, Attendance Theory & Practical: 05-marks,
Assignments: 05-marks)
Oral exam:
Oral examination based on the entire syllabus of CSDO7023 and CSDL7033

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Course Code: Course Title Credit
CSDL7021 User Experience Design with VR Lab 1


Prerequisite: Computer Graphics, Python
Lab Objectives:
1 To perform installation of Unity
2 To explore working of VR Gadget
3 To develop scene VR application
4 To track objects in virtual environment
Lab Outcomes:
1 Setup VR development environment
2 Use HTC Vive/ Google Cardboard/ Google Daydream and Samsung gear VR.
3 Develop VR scene and place object
4 Identify, examine and develop software that reflects fundamental techniques for the design and
deployment of VR experiences

Suggested Experiments:
Sr. No. Name of the Experiment
1 Installation of Unity and Visual Studio, setting up Unity for VR development,
understanding documentation of the same.
2 Demonstration of the working of HTC Vive, Google Cardboard, Google Daydream and
Samsung gear VR.
3 Develop a scene in Unity that includes:
i. a cube, plane and sphere, apply transformations on the 3 game objects.
ii. add a video and audio source
4 Develop a scene in Unity that includes a cube, plane and sphere. Create a new material
and texture separately for three Game objects. Change the colour, material and texture of

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each Game object separately in the scene. Write a C# program in visual studio to change
the colour and material/texture of the game objects dynamically on button click
5 Develop a scene in Unity that includes a sphere and plane . Apply Rigid body
component, material and Box collider to the game Objects. Write a C# program to grab
and throw the sphere using vr controller.
6 Develop a simple UI(User interface ) menu with images, canvas, sprites and button.
Write a C# program to interact with UI menu through VR trigger button such that on
each successful trigger interaction display a score on scene .
7 Create an immersive environment (living room/ battlefield/ tennis court) with only static
game objects. 3D game objects can be created using Blender or use available 3D models
8 Include animation and interaction in the immersive environment created in Assignment
7.
9 Case Study/Mini Project: Create a virtual environment for any use case. The application
must include at least 4 scenes which can be changed dynamically, a good UI, animation
and interaction with game objects. (e.g. VR application to visit a zoo)
10 Presentation of Mini Project

Useful Links
1 https://nptel.ac.in/courses/106106138
2 https://nptel.ac.in/courses/121106013
3 https://www.coursera.org/learn/develop -augmented -virtual -mixed -extended -reality -applicatio
ns-webxr -unity -unreal
4 https://tih.iitr.ac.in/AR -VR.html


Term Work:
1 Term work should consist of 08 experiments and mini project
2 Journal must include at least 2 assignments based on Theory and Practical’s

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3 The final certification and acceptance of term work ensures satisfactory performance of
laboratory work and minimum passing marks in term work.
4 Total 25 Marks (Experiments: 15-marks, Attendance Theory & Practical: 05-marks,
Assignments: 05-marks)
Oral & Practical exam:
Oral examination based on the entire syllabus of CSDO704 and CSL704

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Course Code: Course Title Credit
CSDOL7022 Blockchain Lab 1


Prerequisite: Java, Python, JavaScript.
Lab Objectives:
1 To develop and deploy smart contracts on local Blockchain.
2 To deploy the smart contract on test networks.
3 To deploy and publish smart contracts on Ethereum test network.
4 To design and develop crypto currency.
5 To deploy chain code on permissioned Blockchain.
6 To design and develop a Full-fledged DApp using Ethereum/Hyperledger.
Lab Outcomes:
1 Develop and test smart contract on local Blockchain.
2 Develop and test smart contract on Ethereum test networks.
3 Write and deploy smart contract using Remix IDE and Metamask.
4 Design and develop Cryptocurrency.
5 Write and deploy chain code in Hyperledger Fabric.
6 Develop and test a Full-fledged DApp using Ethereum/Hyperledger.

Suggested Experiments:
Sr. No. Name of the Experiment
1 Local Blockchain: Introduction to Truffle, establishing local Blockchain using Truffle
a) Cryptography in Blockchain and Merkle root tree hash
2 Smart contracts and Chain code : Solidity programming language, chain code
(Java/JavaScript/Go), deployment on Truffle local
a) Creating Smart Contract using Solidity
b) Embedding wallet and transaction using Solidity

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3 Deployment and publishing smart contracts on Ethereum test network: Ethereum
Test networks (Ropsten/Gorelli/Rinkeby), deployment on test networks,
Web3.js/Web3.py for interaction with Ethereum smart contract
a) Blockchain platform ethereum using Geth.
b) Blockchain platform Ganache
4 Remix IDE and Metamask: Smart contract development and deployment using
Metamask and Remix. Design and develop Crypto currency
5 Chain code deployment in Hyperledger Fabric: Chain code deployment in
Hyperledger fabric Mini project: Study required front end tools
6 Case Study on Hyperledger
7 Case Study on Other Blockchain platforms.
8 Creating a blockchain Application
9 Mini -project on Design and Development of a DApps using Ethereum/Hyperledger
Fabric : Implementation of Mini Project,
1. Design, configure and testing of mini project
2. Report submission as per guidelines
3. Implementation and Presentation of Mini Projects

Text Books:
1. Ethereum Smart Contract Development, Mayukh Mukhopadhyay, Packt publication.
2. Solidity Programming Essentials: A Beginner's Guide to Build Smart Contracts for Ethereum
and Blockchain, Ritesh Modi, Packt publication.
3. Hands -on Smart Contract Development with Hyperledger Fabric V2, Matt Zand, Xun Wu and
Mark Anthony Morris, O’Reilly.
Reference Books:

1. Mastering Blockchain, Imran Bashir, Packt Publishing
2. Introducing Ethereum and Solidity, Chris Dannen, APress.
3. Hands -on Blockchain with Hyperledger, Nitin Gaur, Packt Publishing.

Mini project:

1. Students should carry out mini-project in a group of three/four students with a subject
In-charge
2. The group should meet with the concerned faculty during laboratory hours and the

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progress of work discussed must be documented.
3. Each group should perform a detailed literature survey and formulate a problem statement.
4. Each group will identify the hardware and software requirement for their defined mini
project problem statement.
5. Design, develop and test their smart contract/chain code.
6. Each group may present their work in various project competitions and paper presentations
Documentation of the Mini Project

The Mini Project Report can be made on following lines:
1. Abstract
2. Contents
3. List of figures and tables
4. Chapter -1 (Introduction, Literature survey, Problem definition, Objectives, Proposed
Solution, Technology/platform used)
5. Chapter -2 (System design/Block diagram, Flow chart, Software requirements, cost
estimation)
6. Chapter -3 (Implementation snapshots/figures with explanation, code, future directions)
7. Chapter -4 (Conclusion)
8. References


Useful Links
1 https://trufflesuite.com/
2 https://metamask.io/
3 https://remix.ethereum.org/
4 https: //www.hyperledger.org/use/fabric


Term Work:
1 Term work should consist of 08 experiments and mini project
2 Journal must include at least 2 assignments based on Theory and Practical’s
3 The final certification and acceptance of term work ensures satisfactory performance of
laboratory work and minimum passing marks in term work.

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4 Total 25 Marks (Experiments: 15-marks, Attendance Theory & Practical: 05-marks,
Assignments: 05-marks)
Oral & Practical exam:
Oral examination based on the Mini Project, Presentation and CSDO7022 .

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Course Code: Course Title Credit
CSDOL7023 Game Theory for Data Science LAB 1


Prerequisite: Probability , Algebra

Lab Objectives:
1 To understand fundamental game theory concepts.
2 To apply game theory to real-world data science scenarios.
3 To analyze Nash equilibria in different types of games.
4 To investigate mixed strategies and their implications.
5 To learn game theory algorithms and computational tools.
6 To explore applications of game theory in data science.

Lab Outcomes: Learner will be able to
1

2 Gain a solid understanding of fundamental game theory concepts.
Develop the ability to apply game theory principles to real-world data science problems.
3 Analyze and identify Nash equilibria in various game scenarios.
4 Comprehend the implications and applications of mixed strategies in game theory.
5 Acquire practical skills in utilizing game theory algorithms and computational tools.
6 Explore and appreciate the wide range of applications of game theory in data science.

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List of Experiments

Sr. Experiment
No
1. Prisoners dilemma
2. Pure Strategy Nash Equilibrium
3. Extensive Form – Graphs and Trees, Game Trees
4. Strategic Form – Elimination of dominant strategy
5. Minimax theorem, minimax strategies
6. Perfect information games: trees, players assigned to nodes, payoffs, backward Induction,
subgame perfect equilibrium,
7. Imperfect -information games – Mixed Strategy Nash Equilibrium – Finding mixed -strategy
Nash equilibria for zero sum games, mixed versus behavioral strategies.
8. Repeated Games
9. Bayesian Nash equilibrium
10 Implementation of any game for example Tic Tac To , coloring triangle , water jug , 8 queen , 8
puzzle etc (this should be done in group of 3-4 )




Textbooks:
1 An Introduction to Game Theory by Martin J. Osborne
2 M. J. Osborne, An Introduction to Game Theory. Oxford University Press, 2004.

References:
1 M. Machler, E. Solan, S. Zamir, Game Theory, Cambridge University Press, 2013.
2 N. Nisan, T. Roughgarden, E. Tardos, and V. V. Vazirani (Editors), Algorithmic Game
Theory. Cambridge University Press, 2007.
3 A.Dixit and S. Skeath, Games of Strategy, Second Edition. W W Norton & Co Inc,
2004.
4 YoavShoham, Kevin Leyton -Brown, Multiagent Systems: Algorithmic,
Game -Theoretic, and Logical Foundations, Cambridge University Press 2008.
5 Zhu Han, DusitNiyato, WalidSaad, TamerBasar and Are Hjorungnes, “Game Theory
in Wireless and Communication Networks”, Cambridge University Press, 2012.
6 Y.Narahari, “Game Theory and Mechanism Design”, IISC Press, World Scientific.

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Digital References:
1. https://nptel.ac.in/courses/110104063
2. https://onlinecourses.nptel.ac.in/noc19_ge32/preview



Term Work:
1. Term work should consist of 10 experiments.
2. The final certification and acceptance of term work ensures satisfactory performance of laboratory
work and minimum passing marks in term work.
3. The final certification and acceptance of term work ensures satisfactory performance of laboratory
work and minimum passing marks in term work.

4. Total 25 Marks
a. Experiments: 15-marks,
b. Attendance Theory & Practical: 05-marks,
c. Assignment: 05-marks
Oral examination based on the entire syllabus of CSDO7023

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Course Code: Course Title Credit
CSP701 Major Project 1 3


Course Objectives:
1 To acquaint with the process of identifying the needs and converting it into the problem.
2 To familiarize the process of solving the problem in a group.
3 To acquaint with the process of applying basic engineering fundamentals to attempt solutions to the
problems.
4 To inculcate the process of self-learning and research.
Course Outcomes:
1 Identify problems based on societal /research needs.
2 Apply Knowledge and skill to solve societal problems in a group
3 Draw the proper inferences from available results through theoretical/ experimental/simulations
4 Analyse the impact of solutions in societal and environmental context for sustainable
development.
5 Demonstrate capabilities of self-learning in a group, which leads to life long learning.
6 Demonstrate project management principles during project work.

Guidelines:

1. Project Topic Selection and Allocation:
• Project topic selection Process to be defined and followed:
o Project orientation can be given at the end of sixth semester.
o Students should be informed about the domain and domain experts whose guidance
can be taken before selecting projects.
o Student‘s should be recommended to refer papers from reputed conferences/journals
like IEEE, Elsevier, ACM etc. which are not more than 3 years old for review of
literature.
o Dataset selected for the project should be large and realtime
o Students can certainly take ideas from anywhere, but be sure that they should evolve
them in the unique way to suit their project requirements. Students can be informed
to refer Digital India portal, SIH portal or any other hackathon portal forproblem
selection.

• Topics can be finalized with respect to following criterion:
o Topic Selection : The topics selected should be novel in nature (Product based,
Application based or Research based) or should work towards removing the lacuna
in currently existing systems.

Page 83

o Technology Used: Use of latest technology or modern tools can be encouraged. AI,
ML, DL, NNFS, NLP based algorithms can be implemented
o Students should not repeat work done previously (work done in the last three years).
o Project work must be carried out by the group of at least 3 students and maximum 4.
o The project work can be undertaken in a research institute or
organization/Industry/any business establishment. (out -house projects)
o The project proposal presentations can be scheduled according to the domains
and should be judged by faculty who are expert in the domain.
o Head of department and senior staff along with project coordinators will take
decision regarding final selection of projects.
o Guide allocation should be done and students have to submit weekly progress report
to the internal guide.
o Internal guide has to keep track of the progress of the project and also has to maintain
attendance report. This progress report can be used for awarding term work marks.
o In case of industry/ out-house projects, visit by internal guide will be preferred and
external members can be called during the presentation at various levels

2. Project Report Format:

At the end of semester, each group needs to prepare a project report as per the guidelines issued
by the University of Mumbai.

A project report should preferably contain following details:
o Abstract
o Introduction
o Literature Survey/ Existing system
o Limitation Existing system or research gap
o Problem Statement and Objective
o Proposed System
o Analysis/Framework/ Algorithm
o Design details
o Methodology (your approach to solve the problem) Proposed System
o Experimental Set up
o Details of Database or details about input to systems or selected data
o Performance Evaluation Parameters (for Validation)
o Software and Hardware Setup
o Implementation Plan for Next Semester
o Timeline Chart for Term1 and Term -II (Project Management tools can be
used.)
o References

Desirable

Students can be asked to undergo some Certification course (for the technical skill set thatwill be
useful and applicable for projects.)

Page 84

3. Term Work:

Distribution of marks for term work shall be done based on following:
o Weekly Log Report
o Project Work Contribution
o Project Report (Spiral Bound) (both side print)
o Term End Presentation (Internal)
The final certification and acceptance of TW ensures the satisfactory
performance on theaboveaspects.
4. Oral and Practical:
Oral and Practical examination (Final Project Evaluation) of Project 1 should be conducted
byInternal and External examiners approved by University of Mumbai at the end of the
semester.

Suggested quality evaluation parameters are as follows:
o Quality of problem selected
o Clarity of problem definition and feasibility of problem solution
o Relevance to the specialization / industrial trends
o Originality
o Clarity of objective and scope
o Quality of analysis and design
o Quality of written and oral presentation
o Individual as well as teamwork

Page 85

Course Code Course Title Credit
CSC801 Advanced Artificial Intelligence 3



Prerequisite: Engineering Mathematics, Data Structures and Algorithm, Python Programming
Course Objectives:
1 To relate with the basic concepts of Probabilistic Models.
2 To understand the scope of Generative Networks in the field of AI.
3 To recognize various components of Autoencoder Architecture and Training process.
4 To learn the fundamentals of Transfer Learning.
5 Provide students with a comprehensive understanding of ensemble methods and their applications.
6 To explore the nascent applications of AI

Course Outcomes: After successful completion of the course student will be able to
1 Acquire basic knowledge of Probabilistic Models.
2 Analyze the working and architecture for Generative Networks.
3 Interpret various components and various types of Autoencoders
4 Understand various aspects of Transfer Learning.
5 Apply ensemble learning techniques to real -world problems and demonstrate improved predictive
performance.
6 Relate to the nascent technologies in the field of artificial intelligence.
Module Content Hrs
1.0 Generative and Probabilistic Models 08
1.1






1.2 Introduction:
Overview of generative models and their importance in AI, Fundamentals of
Probability theory and generative modeling, Introduction to GANs, VAEs and
other generative models. Significance of generative models, Challenges with
generative models.
Probabilistic Models:
Gaussian Mixture Models (GMMs), Hidden Markov Models (HMMs), Bayesian
Networks, Markov Random Field (MRFs), Probabilistic Graphical Model.
2.0 Generative Adversarial Network 07
2.1 Basics of GAN :
Generative Adversarial Networks (GANs) architecture, The discriminator
model and generator model, Architecture and Training
GANs, Vanilla GAN Architecture. GAN variants and improvements (DCGAN,
WGAN, Conditional GAN, CycleGAN), Challenges - Training instability and
model collapse, GAN applications in image
synthesis and style transfer.
3.0 Variational Autoencoders 07

Page 86

3.1



3.2 Introduction:
Basic components of Variational Autoencoders(VAEs), Architecture and
training of VAEs the loss function, Latent space representation and inference,
Applications of VAEs in image generation.

Types of Autoencoders:
Undercomplete autoencoders, Sparse autoencoders, Contractive autoencoders,
Denoising autoencoders, Variational Autoencoders (for generative modelling)
4.0 Transfer Learning 05
4.1 Introduction to transfer learning
Basic terminologies, Pre -trained model and data sets, Feature extraction and
fine tune transfer learning , Recent advancement in transfer learning : self -
supervised learning and meta learning.
5.0 Ensemble learning 06
5.1 Ensemble Classifiers :
Introduction to Ensemble Methods. Bagging and random forests, Boosting
algorithms : AdaBoost Stacking and blending models, Extreme Gradient
Boosting (XGBoost): XGBoost Regression and classification.
6.0 Nascent Technologies in AI 06
6.1 Convergence of AI with Augmented /
Virtual reality techniques for product and
process development

Limitations of 2D Learning Environments, Evolution of virtual worlds
and immersive technologies, Definition and concepts of Augmented
Reality, Definition and concept of the Metaverse, Characteristics and
components of the Metaverse, Challenges and opportunities in the
Metaverse ecosystem, AI in the realm of emerging quantum computing
paragms

Textbooks:
1 Foster, D., 2022. Generative deep learning . " O'Reilly Media, Inc.".
2 Koller, D. and Friedman, N., 2009. Probabilistic graphical models: principles and techniques . MIT press
3 Goodfellow, I., 2016. Deep Learning -Ian Goodfellow, Yoshua Bengio, Aaron Courville - Google Books
4 Murphy, K.P., 2012. Machine learning: a probabilistic perspective . MIT press
5 Zhou, Z.H., 2012. Ensemble methods: foundations and algorithms . CRC press.

References:
1 Xiong, J., Hsiang, E.L., He, Z., Zhan, T. and Wu, S.T., 2021. Augmented reality and virtual reality displays:
emerging technologies and future perspectives. Light: Science & Applications , 10(1), p.216.
2 Mystakidis, S., 2022. Metaverse. Encyclopedia , 2(1), pp.486 -497
3 Gill, S.S., Xu, M., Ottaviani, C., Patros, P., Bahsoon, R., Shaghaghi, A., Golec, M., Stankovski, V., Wu, H.,
Abraham, A. and Singh, M., 2022. AI for next generation computing: Emerging trends and future directions.
Internet of Things , 19, p.100514

Page 87

4 Mangini, S., Tacchino, F., Gerace, D., Bajoni, D. and Macchiavello, C., 2021. Quantum computing models for
artificial neural networks. Europhysics Letters , 134(1), p.10002.

Digital References:
https://nptel.ac.in/courses/106106201
https://onlinecourses.nptel.ac.in/noc20_cs62/preview
https://machinelearningmastery.com/what -are-generative -adversarial -networks -gans/

Assessment :
Internal Assessment:
Assessment consists of two class tests of 20 marks each. The first class test is to be conducted when
approx. 40% syllabus is completed and second class test when additional 40% syllabus is
completed. Duration of each test shall be one hour.

End Semester Theory Examination:
1 Question paper will comprise of total six questions.
2 All question carries equal marks
3 Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3
then part (b) will be from any module other than module 3)
4 Only Four question need to be solved.
5 In question paper weightage of each module will be proportional to number of respective
lecture hours as mention in the syllabus.

Page 88

Course Code Course Name Total
CSDO8011 AI for financial & Banking application 03


Course Objectives:

Sr.
No Course Objectives
1 To understand the impact of technology and digitization on financial and banking
enterprises.
2 To explore blockchain technologies in the financial sector.
3 To examine digital money transfer mechanisms and GIFT cities.
4 To evaluate the benefits of digitization and cloud services in banking.
5 To analyze enterprise software solutions for financial operations.
6 To study the integration of AI in banking processes

Course Outcomes:

Sr.
No Course Outcomes
On successful completion, of course, learner/student will be able to:
1 Gain knowledge of technology's influence on financial and banking enterprises.
2 Understand the applications of blockchain in the financial sector.
3 Recognize digital money transfer mechanisms and its role in digitization
4 Evaluate the advantages of digitization and cloud services in banking.
5 Analyze enterprise software solutions for financial operations.
6 Explore the integration of AI in banking processes.




DETAILED SYLLABUS:

Sr. No. Module Detailed Content Hours
1
Information Technology
Infrastructure and
Digitization of Financial
Banking Enterprises Digital Technology driven processes, BlockChain
technologies for Financial – Banking sector, GIFT citie
Digital Money transfer Mechanisms. Digitization/ cloud
services and solutions in banking and financial services
Profiling enterprise software’s in financial and banking
enterprises. Building Efficiencies, productivity, and
infallibility in financial & Banking operations. Detailed
study of various processes which shall be transformed by
AI integration in banking and financial services. 04

Page 89

Self-learning : Introduction to business efficiencies,
industrial productivity and high degree reliability systems
for competitive advantage and carbon neutral enterprises.
2
Financial Statistics and
The Sharpe Ratio Probability, Combinatorics, Mathematical Expectation
,Sample Mean, Standard Deviation, and Variance ,Sample
Skewness and Kurtosis ,Sample Covariance and
Correlation ,Financial Returns ,Capital Asset Pricing
Model ,Sharpe Ratio Formula, Time Periods and
Annualizing, Ranking Investment Candidates, The
Quantmod Package, Measuring Income Statement
Growth, Sharpe Ratios for Income Statement Growth 07
3
Cluster Analysis K-Means Clustering, Dissecting the K-Means Algorithm
Sparsity and Connectedness of Undirected Graph
Covariance and Precision Matrices, Visualizing
Covariance, The Wishart distribution Glasso
Penalization for Undirected Graphs, Running the Glasso
Algorithm, Tracking a Value Stock through the Years
Regression on Yearly Sparsity , Regression on Quarterly
Sparsity , Regression on Monthly Sparsity 07
4
Gauging the Market
Sentiment Markov Regime Switching Model, Reading the Market
Data, Bayesian Reasoning, The Beta Distribution , Prior
and Posterior Distributions , Examining Log Returns for
Correlation ,Momentum Graphs ,Simulating Trading
Strategies , Foreign Exchange Markets , Chart Analytics
Initialization and Finalization , Momentum Indicators ,
Bayesian Reasoning within Positions , Entries , Exils
,Profitability,, Short -Term Volatility, The State Machine 07
5 Trading algorithms Vectorized Backtesting, Backtesting an SMA -Based
Strategy, Backtesting a Daily DNN -Based Strategy
Backtesting an Intraday DNN -Based Strategy , Risk
Management : Trading Bot , Vectorized Backtesting
Event -Based Backtesting ,Assessing Risk , Backtesting
Risk Measures , Stop Loss , Trailing Stop Loss , Take
Profit 07
6 Fraud Analytics Introduction , The Analytical Fraud Model Life Cycle ,
Model Representation , Traffic Light Indicator Approach
,Decision Tables , Selecting the Sample to Investigate
,Fraud Alert and Case Management ,Visual Analytics
,Backtesting Analytical Fraud Models : Backtesting Data
Stability ,Backtesting Model Stability ,Backtesting Model
Calibration , Model Design and Documentation 05






Textbooks:
1 Financial Analytics with R Building a Laptop Laboratory for Data Science MARK J.
BENNETT University of Chicago DIRK L. HUGEN University of Iowa
2 Artificial Intelligence in Finance A Python -Based Guide, Yves Hilpisch A
3 Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide
to Data Science for Fraud Detection , Bart Baesens, Veronique Van Vlasselaer, Wouter
Verbeke

Page 90

References:
1 “ Machine Learning for Asset Managers" by Marcos López de Prado
2 "Advances in Financial Machine Learning" by Marcos López de Prado.

Digital References:
1. https ://www.eastnets.com/newsroom/digital -transformation -in-the-banking -and-financial -services -sector
2. https ://www.techopedia.com/definition/34633/generative -ai

Assessment :
Internal Assessment:
Assessment consists of two class tests of 20 marks each. The first class test is to be conducted when
approx. 40% syllabus is completed and second class test when additional 40% syllabus is
completed. Duration of each test shall be one hour.

End Semester Theory Examination:
1 Question paper will comprise of total six questions.
2 All question carries equal marks
3 Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3
then part (b) will be from any module other than module 3)
4 Only Four question need to be solved.
5 In question paper weightage of each module will be proportional to number of respective
lecture hours as mention in the syllabus.

Page 91

Course Code Course Title Credit
CSDO8012 Quantum Computing 3




Prerequisite: Engineering Mathematics, Data Structures and Algorithm, Python Programming
Course Objectives:
1 To understand basics of quantum computing
2 To understand mathematics required for quantum computing
3 To understand building blocks of quantum computing and design algorithms
4 To understand quantum hardware principles and tools for quantum computing.

Course Outcomes: After successful completion of the course student will be able to
1 Understand basic concepts of quantum computing
2 Illustrate building blocks of quantum computing through architecture and
programming models.
3 Appraise various mathematical models required for quantum computing
4 Discuss various quantum hardware building principles.
5 Identify the various quantum algorithms
6 Describe usage of tools for quantum computing.
Module Content Hrs
1.0 Introduction to Quantum Computing 7
1.1 Motivation for studying Quantum Computing
Origin of Quantum Computing
Quantum Computer vs. Classical Computer
Introduction to Quantum mechanics
Overview of major concepts in Quantum Computing
1.2 Qubits and multi -qubits states
Bloch Sphere representation
Quantum Superposition
Quantum Entanglement
Major players in the industry (IBM, Microsoft, Rigetti, D-Wave
etc.)
2.0 Mathematical Foundations for Quantum Computing 05
2.1 Matrix Algebra: basis vectors and orthogonality, inner product and
Hilbert spaces, matrices and tensors, unitary operators and projectors,
Dirac notation, Eigen values and Eigen vectors.
3.0 Building Blocks for Quantum Program 08

Page 92

3.1 Architecture of a Quantum Computing platform
Details of q-bit system of information representation:
Block Sphere
Multi -qubits States Quantum superposition of qubits (valid and
invalid superposition)
Quantum Entanglement
Useful states from quantum algorithmic perceptive e.g. Bell State
Operation on qubits: Measuring and transforming using gates.
Quantum Logic gates and Circuit
No Cloning Theorem and Teleportation
3.2 Programming model for a Quantum Computing Program
Steps performed on classical computer
Steps performed on Quantum Computer
Moving data between bits and qubits.
4.0 Quantum Algorithms and Error correction 06
4.1 Quantum Algorithms, Shor‘s Algorithm, Grover‘s Algorithm.
Deutsch‘s Algorithm, Deutsch -Jozsa Algorithm
Quantum error correction using repetition codes
4.2 3 qubit codes, Shor‘s 9 qubit error correction Code
5.0 Quantum Hardware 10
5.1 Ion Trap Qubits ,The DiVincenzo Criteria , Lagrangian and
Hamiltonian Dynamics in a Nutshell: Dynamics of a Translating
5.2 Rotor
Quantum Mechanics of a Free Rotor: A Poor Person‘s Atomic
5.3 Model: Rotor Dynamics and the Hadamard Gate, Two -Qubit Gates
The Cirac -Zoller Mechanism: Quantum Theory of Simple
Harmonic Motion, A Phonon -Qubit Pair Hamiltonian, Light -
Induced Rotor -Phonon Interactions, Trapped Ion Qubits, Mølmer -
Sørenson Coupling ..
5.4 Cavity Quantum Electrodynamics (cQED): Eigenstates of the
Jaynes -Cummings Hamiltonian
Circuit QED (cirQED): Quantum LC Circuits, Artificial Atoms,
Superconducting Qubits
Quantum computing with spins:
Quantum inverter realized with two exchange coupled spins in
quantum dots, A 2-qubit spintronic universal quantum gate.
6.0 OSS Toolkits for implementing Quantum program 03
6.1 IBM quantum experience
Microsoft Q
Rigetti PyQuil (QPU/QVM)



Textbooks:
1 Michael A. Nielsen, ―Quantum Computation and Quantum Informationǁ, Cambridge
University Press.
2 David McMahon, ―Quantum Computing Explainedǁ, Wiley ,2008
3 Qiskit textbook https://qiskit.org/textbook -beta/
4 Vladimir Silva, Practical Quantum Computing for Developers,2018

Page 93

References:
1 Bernard Zygelman, A First Introduction to Quantum Computing and Information,2018
2 Supriyo Bandopadhyay and Marc Cahy, ―Introduction to Spintronicsǁ, CRC Press, 2008
3 The Second Quantum Revolution: From Entanglement to Quantum Computing and Other
Super -Technologies, Lars Jaeger
4 La Guardia, Giuliano Gladioli ―Quantum Error correction codesǁSpringer,2021

Digital References:
https://onlinecourses.nptel.ac.in/noc21_cs103/preview
https:/ /www.coursera.o rg/courses?query=quantum%20computing
https:/ /www.cl.cam.ac.uk/t eaching/1617/QuantComp/

Assessment :
Internal Assessment:
Assessment consists of two class tests of 20 marks each. The first class test is to be conducted
when approx. 40% syllabus is completed and second class test when additional 40% syllabus is
completed. Duration of each test shall be one hour.

End Semester Theory Examination:
1 Question paper will comprise of total six questions.
2 All question carries equal marks
3 Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3
then part (b) will be from any module other than module 3)
4 Only Four question need to be solved.
5 In question paper weightage of each module will be proportional to number of respective
lecture hours as mention in the syllabus.

Page 94

Course Code: Course Title Credit
CSDO8013 Reinforcement Learning 3


Prerequisite: Mathematical concepts of Geometry, Linear Algebra, Calculus, Basic Electronics
Course Objectives:
1 Learn about robots as an agent of automation and other Use cases
2 Design and Development of robots based on Direct and Inverse Kinematics
3 Learn the different types of Actuators, Sensors, and degree of freedom of Robots
4 Learn the concepts of Motions, Velocities and Dynamic/ force analysis of Robots
5 Learn algorithms governing Robot movements and Robot Programming
6 Learn about integration of electronics and communication devices for multimodal functions
7 Learn about integration of AI in robotics and self-configuring Robots
Course Outcomes:
1 Understand different types of robots, specifications of Robots its characteristics and applications.
2 Understanding Direct – Inverse kinematics of robotic manipulator.
3 Identify actuators, sensors, and control of a robot for different applications
4 Developing the differential relationships of motion, velocities and dynamic analysis of force
5 Developing perspectives on AI and Robotics
6 Developing footprints of algorithms, programming associated with Robots and conceptualizing
self-configuring Robots and use of Robots in different applications



Module Content Hrs
1 Introduction and Fundamentals of Robotics and Automation 4
1.1 Automation and its types, definition of Robotics and a Robot, History of
Robotics, Advantages and Disadvantages of Robot, Robotic Manipulators,
Robot Motions, Robot Anatomy, Links and Joints, Classification of Robots,
Specification of Robot, Applications of Robots
2 Direct and Inverse Kinematics 7

Page 95

2.1 Direct (Forward) Kinematics: Homogeneous coordinates, Link coordinates,
Coordinate frame, coordinate transform, Arm equations, An example – Four
Axis SCARA.
2.2 Inverse Kinematics: Inverse kinematics problem, Tool Configuration, An
example – Four Axis SCARA.
3 Actuators and Sensors 7
3.1 Characteristics of Actuating Systems, Comparison of Actuating Systems,
Hydraulic Devices, Pneumatic Devices, Electric Motors, Magneto strictive
Actuators
3.2 Sensor Characteristics, Position Sensors, Velocity Sensors, Acceleration
Sensors, Force and Pressure Sensors, Torque Sensors, Light and Infrared
Sensors, Touch and Tactile Sensors, Proximity Sensors, Sniff Sensors,
Vision Systems, Voice Synthesizer
4 Motions, velocities and dynamic analysis of force 7
4.1 Differential relationship, Jacobian, Differential motions of a frame and robot,
Inverse Jacobian, Lagrangian mechanics, Moments of Inertia, Dynamic
equations of robots, Transformation of forces and moment between
coordinate frames
5 Self-configuring Robots and AI integration 8
5.1 Historical perspective of AI in Robotics, Uncertainty in Robotics
Reinforcement Learning: Basic overview, examples, elements, Tabular
Solution Methods - Multiarmed bandits, Finite Markov decision process,
Dynamic programming (Policy Evaluation, Policy Iteration, Value
Iteration), Monte Carlo Methods.
6 Applications of Robotics for Automation 6
6.1 Robot Application in Manufacturing: Material Transfer - Material handling,
loading and unloading Processing - spot and continuous arc welding & spray
painting – Assembly Inspection, Selected Embedded System based
Applications: Database Applications (smart cards), Process -Control (Fuzzy
logic), Robot application in Medical, Industrial Automation, Security

Page 96

Textbooks:
1 Robert Shilling, “Fundamentals of Robotics -Analysis and control”, PHI, 2003.
2 Saeed B. Niku, “Introduction to Robotics Analysis, Systems, Applications”,3rd Edition, Wiley,
2019.
3 Saha, S.K., “Introduction to Robotics”, 2nd Edition, McGraw -Hill Higher Education, New Delhi,
2014.
4 Staughard, Robotics and AI, Prentice Hall of India
5 Ashitava Ghoshal, “Robotics -Fundamental Concepts and Analysis”, Oxford University Press,
Sixth impression, 2010
6 Mukherjee S., “Robotics Process Automation”, 1st Edition, Khanna Publishing House, New
Delhi, 2020.
References:
1 John J. Craig, “Introduction to Robotics – Mechanics & Control”, 3rd Edition, Pearson Education,
India, 2009
2 Mark W. Spong & M. Vidyasagar, “Robot Dynamics & Control”, 2nd Wiley India Pvt. Ltd., 2004
3 Principles of Robot Motion – Theory, Algorithms and Implementation by Howie Choset, Lynch,
PHI.


Assessment :
Internal Assessment:
Assessment consists of two class tests of 20 marks each. The first class test is to be conducted when
approx. 40% syllabus is completed and second class test when additional 40% syllabus is completed.
Duration of each test shall be one hour.
End Semester Theory Examination:
1 Question paper will comprise of total six questions.
2 All question carries equal marks
3 Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then
part (b) will be from any module other than module 3)
4 Only Four question need to be solved
5 In question paper weightage of each module will be proportional to number of respective lecture
hours as mention in the syllabus

Page 97

Useful Links
1 https://swayam.gov.in/nc_details/NPTEL
2 https ://www. udemy.com/course/robotics -course/
3 https ://www. coursera.org/courses?query=robotics

Page 98

Course Code Course Name Credit
CSDO8021 Graph Data Science 03


Course Objectives:

Sr.
No Course
Objectives
1 To Understand the basics of graphs, including definitions, connectivity, and properties.
2 To Explore the use of graphs in solving puzzles and optimization problems.
3 To Learn about the advantages of graph databases over relational and NoSQL databases.
4 To Gain knowledge of data modeling with graphs, including the labeled property graph model.
5 To Develop skills in building graph database applications, including data modeling and testing.
6 To Explore real-world use cases and understand non-functional characteristics of graph databases.

Course Outcomes:

Sr.
No Course Outcomes
On successful completion, of course, learner/student will be able to:
1 Demonstrate a solid understanding of graph concepts and properties.
2 Apply graph algorithms to solve puzzles and optimization problems.
3 Compare graph databases with relational and NoSQL databases.
4 Model data using the labeled property graph model and avoid common pitfalls.
5 Build graph database applications with proper data modeling and testing.
6 Analyze and implement graph database solutions for real-world use cases, considering non-functional
characteristics


DETAILED SYLLABUS:

Sr. No. Module Detailed Content Hours
1 Introduction to
Graph Definitions and examples, Three puzzles, Paths and
cycles, Connectivity, Eulerian graphs, Hamiltonian
graphs, shortest path, Chinese postman problem, traveling
salesman problem, trees, properties of trees 04
2 Introduction Graph
databases A High -Level View of the Graph Space, Graph Databases,
Graph Compute Engines, The Power of Graph Databases,
Performance, Flexibility, Agility, Options for Storing
Connected Data, Relational Databases Lack
Relationships, NOSQL Databases Also Lack
Relationships, Graph databases embraces relationship 07
3 Data
Modelling
with Graphs Models and Goals, The Labelled Property Graph Mode
Querying Graphs, A Comparison of Relational and Graph
Modelling, Cross -Domain Models, Common Modelling
Pitfalls, Identifying Nodes and Relationships, Avoiding
Anti-Patterns 07

Page 99

4 Building a Graph
Database Application Data Modelling , Application Architecture ,Testing
,Capacity Planning ,Importing and Bulk Loading Data , 07
5 Graphs in the Real World Organizations Choose Graph Databases, Common Use
Cases, Real-World Examples, Authorization and Acces
Control, Geospatial and Logistics, Graph Database
Internals, Native Graph Processing, Native Graph Storage
Programmatic APIs, Kernel API, Core API, Traversa
Framework, Non-functional Characteristics 07
6 case study Neo4j – About, Neo4j – Installation, Neo4j – Browser
Neo4j - Query Language (Cypher), Neo4j - Create a Node
Neo4j - Create a Relationship, Neo4j - Create an Index
Neo4j - Create a Constraint, Neo4j - Select Data with
MATCH, Neo4j - Import Data from CSV, Neo4j - Drop an
Index, Neo4j - Drop a Constraint, Neo4j - Delete a Node,
Neo4j - Delete a Relationship 05




Textbooks:
1 Introduction to Graph Theory Fourth edition, Robin J. Wilson
2 Daphne Koller and Nir Friedman, "Probabilistic Graphical Models: Principles and Techniques”,
Cambridge, MA: The MIT Press, 2009 (ISBN 978 -0-262-0139 - 2).
3 Graph databases, Ian Robinson, Jim Webber & Emil Eifrem

References:
1
"Graph Databases: New Opportunities for Connected Data" by Ian Robinson, Jim Webber, and
Emil Eifrém.
2 "Neo4j in Action" by Aleksa Vukotic, Nicki Watt, and Tareq Abedrabbo.
3 "Graph Databases for Beginners" by Mark Needham and Amy E. Hodler.
4 "Practical Neo4j" by Gregory Jordan.
5 "Learning Neo4j" by Rik Van Bruggen.
6 "Graph Database Applications and Concepts with Neo4j" by Dionysios Synodinos.

Digital References:
1. https://web4.ensiie.fr/~stefania.dumbrava/OReilly_Graph_Databases.pdf
2. https:/ /www.qua ckit.com/neo4j/tutorial/

Page 100

Assessment :
Internal Assessment:
Assessment consists of two class tests of 20 marks each. The first class test is to be conducted when
approx. 40% syllabus is completed and second class test when additional 40% syllabus is
completed. Duration of each test shall be one hour.

End Semester Theory Examination:
1 Question paper will comprise of total six questions.
2 All question carries equal marks
3 Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3
then part (b) will be from any module other than module 3)
4 Only Four question need to be solved.
5 In question paper weightage of each module will be proportional to number of respective
lecture hours as mention in the syllabus.

Page 101

Course Code: Course Title Credit
CSDO8022 Recommendation Systems 3


Prerequisite: Artificial Intelligence and Machine Learning, Basic knowledge of Python
Course Objectives:
1 To introduce Recommendation systems and it’s basic concepts.
2 To understand design and working of Collaborative Filtering based recommendation.
3 To analyze design and working of Content -based recommendation.
4 To understand design and working of Knowledge based recommendation.
5 To understand design and working of Ensembled - Based and Hybrid Recommendation Systems.
6 To identify the methods for evaluation of recommendation systems.
Course Outcomes: After successful completion of the course student will be able to
1 To have a broad understanding of the field of Recommendation Systems.
2 In-depth Knowledge of the architecture and models for Collaborative Filtering.
3 Understanding the architecture and working of Content based recommendation systems.
4 Understanding the architecture and basics of Knowledge based recommendation systems.
5 Analyzing hybrid and ensembles recommendation systems.
6 Evaluation of recommendation systems by selecting right evaluation parameter.

Module Content Hrs
1.0 Introduction to Recommendation System 06
1.1



1.2 History of recommendation system, Eliciting Ratings and other Feedback
Contributions, Implicit and Implicit Ratings, Recommender system
functions.

Linear Algebra notation: Matrix addition, Multiplication, transposition, and
inverses; covariance matrices, Understanding ratings, Applications of
recommendation systems, Issues with recommender system .
2.0 Collaborative Filtering 06
2.1 Architecture of Collaborative Filtering, User -based nearest neighbour
recommendation, Item-based nearest neighbour recommendation, Model
based and pre-processing based approaches, Clustering for recommendation
system, Attacks on collaborative recommender systems, Advantages and
drawbacks of Collaborative Filtering.

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3.0 Content -based recommendation 07
3.1




3.2 Architecture of content -based systems, Content representation and content
similarity, Item profiles, Discovering features of documents, Obtaining item
features from tags, Representing item profiles, Methods for learning user
profiles, Similarity based retrieval, The Role of User Generated Content in
the Recommendation Process.

Bayes classifier for recommendation, Regression based recommendation
system. Advantages and drawbacks of content -based filtering
4.0 Knowledge based recommendation 06
4.1 Knowledge representation and reasoning, Constraint based recommenders,
Case based recommenders, Persistent Personalization in Knowledge -Based
Systems, Conversational Recommendation. Search based recommendation,
Navigation -based recommendation.
5.0 Ensembled - Based and Hybrid Recommendation System 06
5.1 Opportunities for hybridization, Monolithic hybridization design: Feature
combination, Feature augmentation, Parallelized hybridization design:
Weighted, Switching, Mixed, Pipelined hybridization design: Cascade Meta -
level, Limitations of hybridization strategies.
6.0 Evaluating Recommendation System 08
6.1



6.2 Characteristics and properties of evaluation research, Evaluation design
goals - Accuracy, Coverage, Confidence and Trust, Novelty, Serendipity,
Diversity, Robustness, Stability and Scalability.

Comparison between evaluation design of classification model and
recommendation system, Error metrics, Decision -Support metrics, User -
Centred metrics. Comparative analysis between different types of
recommendation systems.

Textbooks:
1 Jannach, D., Zanker, M., Felfernig, A., & Friedrich, G. (2010). Recommender systems: an introduction .
Cambridge University Press.
2 Ricci, F., Rokach, L., & Shapira, B. (2011). Introduction to Recommender Systems Handbook. Springer,
Boston, MA.
References:
1 Aggarwal, C. C. (2016). Recommender systems (Vol. 1). Cham: Springer International Publishing.
Online References:

Page 103

1 http://www.iem.iitkgp.ac.in/eco/Recommender_Systems/
2 https:/ /www.coursera .org/specializations/re commender -systems
3 https:/ /www.udemy.com/course/re commender -systems/
4 https:/ /www.analyticsvidhya.com/blog/2021/08/developing -a-course -recommender -system -
using -python/


Assessment :
Internal Assessment:
Assessment consists of two class tests of 20 marks each. The first class test is to be conducted when
approx. 40% syllabus is completed and second class test when additional 40% syllabus is completed.
Duration of each test shall be one hour.
End Semester Theory Examination:
1 Question paper will comprise of total six questions.
2 All question carries equal marks
3 Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then
part (b) will be from any module other than module 3)
4 Only Four questions need to be solved.
5 In question paper weightage of each module will be proportional to number of respective lecture
hours as mention in the syllabus

Page 104

Course Code Course Name Credit
CSDO8023 Social Media Analytics 03

Prerequisite: Graph Theory, Data Mining, Python/R programming

Course Objectives: The course aims:
1 Familiarize the learners with the concept of social media.
2 Familiarize the learners with the concept of social media analytics and understand
its significance.
3 Enable the learners to develop skills required for analyzing the effectiveness of
social media.
4 Familiarize the learners with different tools of social media analytics.
5 Familiarize the learner with different visualization techniques for Social media
analytics.
6 Examine the ethical and legal implications of leveraging social media data.

Course Outcomes:
1 Understand the concept of Social media
2 Understand the concept of social media Analytics and its significance.
3 Learners will be able to analyze the effectiveness of social media
4 Learners will be able to use different Social media analytics tools effectively and
efficiently.
5 Learners will be able to use different effective Visualization techniques to represent
social media analytics.
6 Acquire the fundamental perspectives and hands -on skills needed to work with
social media data.

Module Detailed Content Hours
1. Social Media Analytics: An Overview
Core Characteristics of Social Media, Types of Social Media, Social media
landscape, Need for Social Media Analytics (SMA), SMA in small & large
organizations.
Purpose of Social Media Analytics, Social Media vs. Traditional Business
Analytics, Seven Layers of Social Media Analytics, Types of Social Media
Analytics, Social Media Analytics Cycle, Challenges to Social Media
Analytics, Social Media Analytics Tools 6
2. Social Network Structure, Measures & Visualization
Basics of Social Network Structure - Nodes, Edges & Tie Describing the
Networks Measures - Degree Distribution, Density, Connectivity,
Centralization, Tie Strength & Trust
Network Visualization - Graph Layout, Visualizing Network
features, Scale Issues.
Social Media Network Analytics - Common Network Terms,
Common Social Media Network Types, Types of Networks,
Common Network Terminologies, Network Analytics Tools. 6
3. Social Media Text, Action & Hyperlink Analytics
Social Media Text Analytics - Types of Social Media Text, Purpose
of Text Analytics, Steps in Text Analytics, Social Media Text 8

Page 105

Analysis Tools
Social Media Action Analytics - What Is Actions Analytics?
Common Social Media Actions, Actions Analytics Tools
Social Media Hyperlink Analytics - Types of Hyperlinks, Types of
Hyperlink Analytics, Hyperlink Analytics Tools
4. Social Media Location & Search Engine Analytics
Location Analytics - Sources of Location Data, Categories of
Location Analytics, Location Analytics and Privacy Concerns,
Location Analytics Tools
Search Engine Analytics - Types of Search Engines, Search Engine
Analytics, Search Engine Analytics Tools 6
5. Social Information Filtering
Social Information Filtering - Social Sharing and filtering ,
Automated Recommendation systems, Traditional Vs social
Recommendation Systems
Understanding Social Media and Business Alignment, Social Media KPI,
Formulating a Social Media Strategy, Managing Social Media Risks 6
6. Social Media Analytics Applications and Privacy
Social media in public sector - Analyzing public sector social media, analyzing
individual users, case study.
Business use of Social Media - Measuring success, Interaction and
monitoring, case study.
Privacy - Privacy policies, data ownership and maintaining privacy
online. 7


Textbooks:
1. Seven Layers of Social Media Analytics_ Mining Business Insights from Social Media Text,
Actions, Networks, Hyperlinks, Apps, Search Engine, and Location Data, Gohar
F. Khan,(ISBN -10: 1507823207).
2. Analyzing the Social Web 1st Edition by Jennifer Golbeck
3. Mining the Social Web_ Analyzing Data from Facebook, Twitter, LinkedIn, and
Other Social Media Sites, Matthew A Russell, O‘Reilly
4 Charu Aggarwal (ed.), Social Network Data Analytics, Springer, 2011
References:
1. Social Media Analytics [2015], Techniques and Insights for Extracting Business Value
Out of Social Media, Matthew Ganis, AvinashKohirkar, IBM Press
2. Social Media Analytics Strategy_ Using Data to Optimize Business Performance, Alex
Gonçalves, APress Business Team
3. Social Media Data Mining and Analytics, Szabo, G., G. Polatkan, O. Boykin & A.
Chalkiopoulus (2019), Wiley, ISBN 978-1-118-82485 -6

Useful Links
1 https://cse.iitkgp.ac.in/~pawang/courses/SC16.html
2 https://onlinecourses.nptel.ac.in/noc20_cs78/preview
3 https://nptel.ac.in/courses/106106146
4 https://7layersanalytics.com/

Page 106

Assessment:
Internal Assessment:
Assessment consists of two class tests of 20 marks each. The first-class test is to be
conducted when approx. 40% syllabus is completed and second class test when additional40%
syllabus is completed. Duration of each test shall be one hour.
End Semester Theory Examination:
1 Question paper will consist of 6 questions, each carrying 20 marks.
2 The students need to solve a total of 4 questions.
3 Question No.1 will be compulsory and based on the entire syllabus.
4 Remaining question (Q.2 to Q.6) will be selected from all the modules.

Page 107

Course Code Course Name Credits
ILO8021 Project Management 03


Objectives:
1. To familiarize the students with the use of a structured methodology/approach for each and every
unique project undertaken, including utilizing project management concepts, tools and techniques.
2. To appraise the students with the project management life cycle and make them kn owledgeable about
the various phases from project initiation through closure.

Outcomes: Learner will be able to…
1. Apply selection criteria and select an appropriate project from different options.
2. Write work break down structure for a project and develop a schedule based on it.
3. Identify opportunities and threats to the project and decide an approach to deal with them
strategically.
4. Use Earned value technique and determine & predict status of the project.
5. Capture lessons learned during project phases and document them for future reference



Module
Detailed Contents
Hrs


01 Project Management Foundation:
Definition of a project, Project Vs Operations, Necessity of project management, Triple
constraints, Project life cycles (typical & atypical) Project phases and stage gate process.
Role of project manager. Negotiations and resolving conflicts. Project management in
various organization structures. PM knowledge
areas as per Project Management Institute (PMI).

5


02 Initiating Projects:
How to get a project started, Selecting project strategically, Project selection models
(Numeric /Scoring Models and Non -numeric models), Project portfolio process, Project
sponsor and creating charter; Project proposal. Effective project team, Stages of team
development & growth (forming, storming, norming &
performing), team dynamics.

6


03 Project Planning and Scheduling:
Work Breakdown structure (WBS) and linear responsibility chart, Interface
Co-ordination and concurrent engineering, Project cost estimation and budgeting, Top
down and bottoms up budgeting, Networking and Scheduling techniques. PERT, CPM,
GANTT chart. Introduction to Project Management
Information System (PMIS).

8


04 Planning Projects:
Crashing project time, Resource loading and leveling, Goldratt's critical chain, Project
Stakeholders and Communication plan.
Risk Management in projects: Risk management planning, Risk identification
and risk register. Qualitative and quantitative risk assessment, Probability and impact
matrix. Risk response strategies for positive and negative risks

6
05 5.1 Executing Projects:
Planning monitoring and controlling cycle. Information needs and reporting, 8

Page 108

engaging with all stakeholders of the projects.
Team management, communication and project meetings.
Monitoring and Controlling Projects:
Earned Value Management techniques for measuring value of work completed; Using
milestones for measurement; change requests and scope creep. Project audit.
Project Contracting
Project procurement management, contracting and outsourcing,




06 Project Leadership and Ethics:
Introduction to project leadership, ethics in projects.
Multicultural and virtual projects.
Closing the Project:
Customer acceptance; Reasons of project termination, Various types of project
terminations (Extinction, Addition, Integration, Starvation), Process of project
termination, completing a final report; doing a lessons learned analysis; acknowledging
successes and failures; Project management templates and other
resources; Managing without authority; Areas of further study.



6




REFERENCES:

1. Jack Meredith & Samuel Mantel, Project Management: A managerial approach, Wiley India, 7thEd.
2. A Guide to the Project Management Body of Knowledge (PMBOK ® Guide), 5th Ed,Project
Management Institute PA, USA
3. Gido Clements, Project Management, Cengage Learning.
4. Gopalan, Project Management, , Wiley India
5. Dennis Lock, Project Management, Gower Publishing England, 9 th Ed.

Assessment :

Internal:
Assessment consists of two tests out of which; one should be compulsory class test and the other is either a
class test or assignment on live problems or course project.

End Semester Theory Examination:
Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question papers
of end semester examination. In question paper weightage of each module will be proportional to number of
respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then part (b)
will be from any module other than module 3)
4. Only Four question need to be solved.

Page 109

Course Code Course Name Credits
ILO8022 Finance Management 03


Objectives:
1. Overview of Indian financial system, instruments and market
2. Basic concepts of value of money, returns and risks, corporate finance, working capital and its
management
3. Knowledge about sources of finance, capital structure, dividend policy

Outcomes: Learner will be able to…
1. Understand Indian finance system and corporate finance
2. Take investment, finance as well as dividend decisions



Module
Detailed Contents
Hrs





01 Overview of Indian Financial System: Characteristics, Components and Functions of
Financial System.
Financial Instruments: Meaning, Characteristics and Classification of Basic Financial
Instruments — Equity Shares, Preference Shares, Bonds -Debentures, Certificates of
Deposit, and Treasury Bills.
Financial Markets: Meaning, Characteristics and Classification of Financial Markets
— Capital Market, Money Market and Foreign Currency Market Financial
Institutions: Meaning, Characteristics and Classification of Financial Institutions —
Commercial Banks, Investment -Merchant Banks a nd Stock
Exchanges



06



02 Concepts of Returns and Risks: Measurement of Historical Returns and Expected
Returns of a Single Security and a Two -security Portfolio; Measurement of Historical
Risk and Expected Risk of a Single Security and a Two-security Portfolio.
Time Value of Money: Future Value of a Lump Sum, Ordinary Annuity, and Annuity
Due; Present Value of a Lump Sum, Ordinary Annuity, and Annuity
Due; Continuous Compounding and Continuous Discounting.


06



03 Overview of Corporate Finance: Objectives of Corporate Finance; Functions of
Corporate Finance —Investment Decision, Financing Decision, and Dividend Decision.
Financial Ratio Analysis: Overview of Financial Statements —Balance Sheet, Profit and
Loss Account, and Cash Flow Statement; Purpose of Financial Ratio Analysis; Liquidity
Ratios; Efficiency or Activity Ratios; Profitability Ratios;
Capital Structure Ratios; Stock Market Ratios; Limitations of Ratio Analysis.


09


04 Capital Budgeting: Meaning and Importance of Capital Budgeting; Inputs for Capital
Budgeting Decisions; Investment Appraisal Criterion —Accounting Rate of Return,
Payback Period, Discounted Payback Period, Net Present Value(NPV), Profitability
Index, Intern al Rate of Return (IRR), and Modified
Internal Rate of Return (MIRR)

10

Page 110

Working Capital Management: Concepts of Meaning Working Capital;
Importance of Working Capital Management; Factors Affecting an Entity’s Working
Capital Needs; Estimation of Working Capital Requirements; Management of
Inventories; Management of Receivables; and Management of Cash and Marketable
Securities.




05 Sources of Finance: Long Term Sources —Equity, Debt, and Hybrids; Mezzanine
Finance; Sources of Short Term Finance —Trade Credit, Bank Finance, Commercial
Paper; Project Finance.
Capital Structure: Factors Affecting an Entity’s Capital Structure; Overview of Capital
Structure Theories and Approaches — Net Income Approach, Net Operating Income
Approach; Traditional Approach, and Modigliani -Miller Approach. Relation between
Capital Structure and Corporate Value; Concept of
Optimal Capital Structure


05

06 Dividend Policy: Meaning and Importance of Dividend Policy; Factors Affecting an
Entity’s Dividend Decision; Overview of Dividend Policy Theories and Approaches —
Gordon’s Approach, Walter’s Approach, and Modigliani -
Miller Approach
03



REFERENCES:

1. Fundamentals of Financial Management, 13th Edition (2015) by Eugene F. Brigham and Joel F.
Houston; Publisher: Cengage Publications, New Delhi.
2. Analysis for Financial Management, 10th Edition (2013) by Robert C. Higgins; Publishers:
McGraw Hill Education, New Delhi.
3. Indian Financial System, 9th Edition (2015) by M. Y. Khan; Publisher: McGraw Hill
Education, New Delhi.
4. Financial Management, 11th Edition (2015) by I. M. Pandey; Publisher: S. Chand (G/L) &
Company Limited, New Delhi.

Assessment :

Internal:
Assessment consists of two tests out of which; one should be compulsory class test and the other is either a
class test or assignment on live problems or course project.

End Semester Theory Examination:
Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question papers
of end semester examination. In question paper weightage of each module will be proportional to number of
respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then part (b)
will be from any module other than module 3)
4. Only Four question need to be solved.

Page 111

Course Code Course Name Credits
ILO8023 Entrepreneurship Development and Management 03


Objectives:
1. To acquaint with entrepreneurship and management of business
2. Understand Indian environment for entrepreneurship
3. Idea of EDP, MSME

Outcomes: Learner will be able to…
1. Understand the concept of business plan and ownerships
2. Interpret key regulations and legal aspects of entrepreneurship in India
3. Understand government policies for entrepreneurs



Module
Detailed Contents
Hrs



01 Overview Of Entrepreneurship: Definitions, Roles and Functions/Values of
Entrepreneurship, History of Entrepreneurship Development, Role of Entrepreneurship in
the National Economy, Functions of an Entrepreneur, Entrepreneurship and Forms of
Business Ownership
Role of Money and Capital Markets in Entrepreneurial Development:
Contribution of Government Agencies in Sourcing information for Entrepreneurship

04



02 Business Plans And Importance Of Capital To Entrepreneurship: Preliminary and
Marketing Plans, Management and Personnel, Start -up Costs and Financing as well as
Projected Financial Statements, Legal Section, Insurance, Suppliers and Risks,
Assumptions and Conclusion, Capital and its Importance to the Entrepreneur
Entrepreneurship And Business Development: Starting a New Business,
Buying an Existing Business, New Product Development, Business Growth and the
Entrepreneur Law and its Relevance to Business Operations


09

03 Women’s Entrepreneurship Development, Social entrepreneurship -role and need, EDP
cell, role of sustainability and sustainable development for SMEs,
case studies, exercises 05



04 Indian Environment for Entrepreneurship: key regulations and legal aspects ,
MSMED Act 2006 and its implications, schemes and policies of the Ministry of MSME,
role and responsibilities of various government organizations, departments, banks etc.,
Role of State governments in terms of infrastructure developments and support etc.,
Public private partnerships, National Skill
development Mission, Credit Guarantee Fund, PMEGP, discussions, group exercises etc

08

05 Effective Management of Business: Issues and problems faced by micro and small
enterprises and effective management of M and S enterprises (risk
management, credit availability, technology innovation, supply chain
management, linkage with large industries), exercises, e-Marketing
08

06 Achieving Success In The Small Business: Stages of the small business life cycle, four
types of firm -level growth strategies, Options – harvesting or closing small business
Critical Success factors of small business
05

Page 112

REFERENCES:

1. Poornima Charantimath, Entrepreneurship development - Small Business Enterprise, Pearson
2. Education Robert D Hisrich, Michael P Peters, Dean A Shapherd, Entrepreneurship, latest edition, The
McGrawHill Company
3. Dr TN Chhabra, Entrepreneurship Development, Sun India Publications, New Delhi
4. Dr CN Prasad, Small and Medium Enterprises in Global Perspective, New century Publications, New
Delhi
5. Vasant Desai, Entrepreneurial development and management, Himalaya Publishing House
6. Maddhurima Lall, Shikah Sahai, Entrepreneurship, Excel Books
7. Rashmi Bansal, STAY hungry STAY foolish, CIIE, IIM Ahmedabad
8. Law and Practice relating to Micro, Small and Medium enterprises, Taxmann Publication Ltd.
9. Kurakto, Entrepreneurship - Principles and Practices, Thomson Publication
10. Laghu Udyog Samachar
11. www.msme.gov.in
12. www.dcmesme.gov.in
13. www.msmetraining.gov.in


Assessment :

Internal:
Assessment consists of two tests out of which; one should be compulsory class test and the other is either a
class test or assignment on live problems or course project.

End Semester Theory Examination:
Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question papers
of end semester examination. In question paper weightage of each module will be proportional to number of
respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then part (b)
will be from any module other than module 3)
4. Only Four question need to be solved.

Page 113

Course Code Course Name Credits
ILO8024 Human Resource Management 03


Objectives:
1. To introduce the students with basic concepts, techniques and practices of the human resource
management.
2. To provide opportunity of learning Human resource management (HRM) processes, related with the
functions, and challenges in the emerging perspective of today’s organizations.
3. To familiarize the students about the latest developments, trends & different aspects of HRM.
4. To acquaint the student with the importance of inter -personal & inter -group behavioral skills in an
organizational setting required for future stable engineers, leaders and managers.

Outcomes: Learner will be able to…
1. Understand the concepts, aspects, techniques and practices of the human resource management.
2. Understand the Human resource management (HRM) processes, functions, changes and challenges in
today’s emerging organizational perspective.
3. Gain knowledge about the latest developments and trends in HRM.
4. Apply the knowledge of behavioral skills learnt and integrate it with in inter personal and intergroup
environment emerging as future stable engineers and managers.



Module
Detailed Contents
Hrs



01 Introduction to HR
• Human Resource Management - Concept, Scope and Importance,
Interdisciplinary Approach Relationship with other Sciences,
Competencies of HR Manager, HRM functions.
• Human resource development (HRD): changing role of HRM – Human
resource Planning, Technological change, Restructuring andrightsizing,
Empowerment, TQM, Managing ethical issues.


5







02 Organizational Behavior (OB)
• Introduction to OB Origin, Nature and Scope of Organizational Behavior,
Relevance to Organizational Effectiveness and Contemporary issues
• Personality: Meaning and Determinants of Personality, Personality
development, Personality Types, Assessment of Personality Traits for
Increasing Self Awareness
• Perception: Attitude and Value, Effect of perception on Individual
Decision -making, Attitude and Behavior.
• Motivation: Theories of Motivation and their Applications for
Behavioral Change (Maslow, Herzberg, McGregor);
• Group Behavior and Group Dynamics: Work groups formal and informal
groups and stages of group development. Team Effectiveness: High performing
teams, Team Roles, cross functional and self-directed team.
• Case study






7

03 Organizational Structure &Design
• Structure, size, technology, Environment of organization; Organizational Roles
& conflicts: Concept of roles; role dynamics; role conflicts and
6

Page 114

stress.
• Leadership: Concepts and skills of leadership, Leadership and
managerial roles, Leadership styles and contemporary issues in
leadership.
• Power and Politics: Sources and uses of power; Politics atworkplace,
Tactics and strategies.



04 Human resource Planning
• Recruitment and Selection process, Job -enrichment, Empowerment - Job-
Satisfaction, employee morale.
• Performance Appraisal Systems: Traditional & modern methods,
Performance Counseling, Career Planning.
• Training & Development: Identification of Training Needs, Training
Methods


5



05 Emerging Trends in HR
• Organizational development; Business Process Re -engineering (BPR), BPR
as a tool for organizational development , managing processes &
transformation in HR. Organizational Change, Culture, Environment
• Cross Cultural Leadership and Decision Making : Cross Cultural
Communication and diversity at work , Causes of diversity, managing
diversity with special reference to handicapped, women and ageing
people, intra company cultural difference in employee motivation.


6





06 HR & MIS
Need, purpose, objective and role of information system in HR, Applications in HRD in
various industries (e.g. manufacturing R&D, Public Transport, Hospitals, Hotels and
service industries
Strategic HRM
Role of Strategic HRM in the modern business world, Concept of Strategy,
Strategic Management Process, Approaches to Strategic Decision Making;
Strategic Intent – Corporate Mission, Vision, Objectives and Goals
Labor Laws & Industrial Relations
Evolution of IR, IR issues in organizations, Overview of Labor Laws in India;
Industrial Disputes Act, Trade Unions Act, Shops and Establishments Act



10


REFERENCES:

1. Stephen Robbins, Organizational Behavior, 16th Ed, 2013
2. V S P Rao, Human Resource Management, 3rd Ed, 2010, Excelpublishing
3. Aswathapa, Human resource management: Text & cases, 6th edition, 2011
4. C. B. Mamoria and S V Gankar, Dynamics of Industrial Relations in India, 15th Ed, 2015, Himalaya
Publishing, 15thedition, 2015
5. P. Subba Rao, Essentials of Human Resource management and Industrial relations, 5th Ed, 2013,
Himalaya Publishing
6. Laurie Mullins, Management & Organizational Behavior, Latest Ed, 2016, Pearson Publications

Assessment :

Internal:
Assessment consists of two tests out of which; one should be compulsory class test and the other is either a
class test or assignment on live problems or course project.

Page 115

End Semester Theory Examination:
Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question papers
of end semester examination. In question paper weightage of each module will be proportional to number of
respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then part (b)
will be from any module other than module 3)
4. Only Four question need to be solved.

Page 116

Course Code Course Name Credits
ILO8025 Professional Ethics and Corporate Social Responsibility
(CSR) 03

Objectives:
1. To understand professional ethics in business
2. To recognized corporate social responsibility
Outcomes: Learner will be able to…
1. Understand rights and duties of business
2. Distinguish different aspects of corporate social responsibility
3. Demonstrate professional ethics
4. Understand legal aspects of corporate social responsibility


Module
Detailed Contents
Hrs

01 Professional Ethics and Business: The Nature of Business Ethics; Ethical
Issues in Business; Moral Responsibility and Blame; Utilitarianism: Weighing Social
Costs and Benefits; Rights and Duties of Business 04


02 Professional Ethics in the Marketplace: Perfect Competition; Monopoly
Competition; Oligopolistic Competition; Oligopolies and Public Policy Professional
Ethics and the Environment: Dimensions of Pollution and Resource Depletion; Ethics
of Pollution Control; Ethics of Conserving
Depletable Resources

08


03 Professional Ethics of Consumer Protection: Markets and Consumer Protection;
Contract View of Business Firm’s Duties to Consumers; Due Care Theory; Advertising
Ethics; Consumer Privacy
Professional Ethics of Job Discrimination: Nature of Job Discrimination;
Extent of Discrimination; Reservation of Jobs.

06

04 Introduction to Corporate Social Responsibility: Potential Business Benefits —Triple
bottom line, Human resources, Risk management, Supplier relations; Criticisms and
concerns —Nature of business; Motives; Misdirection.
Trajectory of Corporate Social Responsibility in India
05

05 Corporate Social Responsibility: Articulation of Gandhian Trusteeship Corporate
Social Responsibility and Small and Medium Enterprises (SMEs) in India, Corporate
Social Responsibility and Public -Private Partnership (PPP) in
India
08


06 Corporate Social Responsibility in Globalizing India: Corporate Social Responsibility
Voluntary Guidelines, 2009 issued by the Ministry of Corporate Affairs, Government of
India, Legal Aspects of Corporate Social
Responsibility —Companies Act, 2013.
08

Page 117

REFERENCES:

1. Business Ethics: Texts and Cases from the Indian Perspective (2013) by Ananda Das Gupta; Publisher:
Springer.
2. Corporate Social Responsibility: Readings and Cases in a Global Context (2007) by Andrew Crane,
Dirk Matten, Laura Spence; Publisher: Routledge.
3. Business Ethics: Concepts and Cases, 7th Edition (2011) by Manuel G. Velasquez; Publisher: Pearson,
New Delhi.
4. Corporate Social Responsibility in India (2015) by BidyutChakrabarty, Routledge, New Delhi.
Assessment :

Internal:
Assessment consists of two tests out of which; one should be compulsory class test and the other is either a
class test or assignment on live problems or course project.

End Semester Theory Examination:
Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question papers
of end semester examination. In question paper weightage of each module will be proportional to number of
respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then part (b)
will be from any module other than module 3)
4. Only Four question need to be solved.

Page 118

Course Code Course Name Credits
ILO8026 Research Methodology 03


Objectives:
1. To understand Research and Research Process
2. To acquaint students with identifying problems for research and develop research strategies
3. To familiarize students with the techniques of data collection, analysis of data and interpretation
Outcomes: Learner will be able to…
1. Prepare a preliminary research design for projects in their subject matter areas
2. Accurately collect, analyze and report data
3. Present complex data or situations clearly
4. Review and analyze research findings



Module
Detailed Contents
Hrs



01 Introduction and Basic Research Concepts
Research – Definition; Concept of Construct, Postulate, Proposition, Thesis, Hypothesis,
Law, Principle.Research methods vs Methodology
Need of Research in Business and Social Sciences
Objectives of Research
Issues and Problems in Research
Characteristics of Research:Systematic, Valid, Verifiable, Empirical and Critical


09



02 Types of Research
Basic Research
Applied Research
Descriptive Research
Analytical Research
Empirical Research
2.6 Qualitative and Quantitative Approaches


07

03 Research Design and Sample Design
Research Design – Meaning, Types and Significance
Sample Design – Meaning and Significance Essentials of a good sampling Stages in
Sample Design Sampling methods/techniques Sampling Errors
07





04 Research Methodology
4.1 Meaning of Research Methodology
4.2. Stages in Scientific Research Process:
a. Identification and Selection of Research Problem
b. Formulation of Research Problem
c. Review of Literature
d. Formulation of Hypothesis
e. Formulation of research Design
f. Sample Design
g. Data Collection
h. Data Analysis
i. Hypothesis testing and Interpretation of Data




08

Page 119

j. Preparation of Research Report

05 Formulating Research Problem
5.1 Considerations: Relevance, Interest, Data Availability, Choice of data, Analysis of
data, Generalization and Interpretation of analysis
04

06 Outcome of Research
04 Preparation of the report on conclusion reached
Validity Testing & Ethical Issues
Suggestions and Recommendation




REFERENCES:

1. Dawson, Catherine, 2002, Practical Research Methods, New Delhi, UBS Publishers Distributors.
2. Kothari, C.R.,1985, Research Methodology -Methods and Techniques, New Delhi, Wiley Eastern
Limited.
3. Kumar, Ranjit, 2005, Research Methodology -A Step-by-Step Guide for Beginners, (2nded), Singapore,
Pearson Education



Assessment :

Internal:
Assessment consists of two tests out of which; one should be compulsory class test and the other is either a
class test or at least 6 assignment on complete syllabus or course project.

End Semester Theory Examination:
Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question papers
of end semester examination. In question paper weightage of each module will be proportional to number of
respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then part (b)
will be from any module other than module 3)
4. Only Four question need to be solved.

Page 120

Course Code Course Name Credits
ILO8027 IPR and Patenting 03

Objectives:
1. To understand intellectual property rights protection system
2. To promote the knowledge of Intellectual Property Laws of India as well as International treaty
procedures
3. To get acquaintance with Patent search and patent filing procedure and applications

Outcomes: Learner will be able to…
1. understand Intellectual Property assets
2. assist individuals and organizations in capacity building
3. work for development, promotion, protection, compliance, and enforcement of Intellectual Property and
Patenting


Module
Detailed Contents
Hr



01 Introduction to Intellectual Property Rights (IPR) : Meaning of IPR, Different
category of IPR instruments - Patents, Trademarks,Copyrights, Industrial Designs, Plant
variety protection, Geographical indications,Transfer of technology etc.
Importance of IPR in Modern Global Economic Environment: Theories of IPR,
Philosophical aspects of IPR laws, Need for IPR, IPR as an instrument of
development


05




02 Enforcement of Intellectual Property Rights: Introduction, Magnitude of problem,
Factors that create and sustain counterfeiting/piracy, International agreements,
International organizations (e.g. WIPO, WTO) activein IPR enforcement
Indian Scenario of IPR: Introduction, History of IPR in India, Overview of IP laws in
India, Indian IPR, Administrative Machinery, Major international treaties signed by India,
Procedure for submitting patent and Enforcement of IPR at
national level etc.



07
03 Emerging Issues in IPR: Challenges for IP in digital economy, e -commerce, human
genome, biodiversity and traditional knowledge etc. 05


04 Basics of Patents: Definition of Patents, Conditions of patentability, Patentable and non -
patentable inventions, Types of patent applications (e.g. Patent of addition etc), Process
Patent and Product Patent, Precautions while patenting, Patent specification Patent claims,
Disclosures and non-disclosures, Paten t rights
and infringement, Method of getting a patent

07

05 Patent Rules: Indian patent act, European scenario, US scenario, Australia scenario,
Japan scenario, Chinese scenario, Multilateral treaties where India is a member (TRIPS
agreement, Paris convention etc.)
08

06 Procedure for Filing a Patent (National and International): Legislation and Salient
Features, Patent Search, Drafting and Filing Patent Applications, Processing of patent,
Patent Litigation, Patent Publicationetc, Time frame and
cost, Patent Licensing, Patent Infringement
07

Page 121

Patent databases: Important websites, Searching international databases


REFERENCE BOOKS:

1. Rajkumar S. Adukia, 2007, A Handbook on Laws Relating to Intellectual Property Rights in India, The
Institute of Chartered Accountants of India
2. Keayla B K, Patent system and related issues at a glance, Published by National Working Group on
Patent Laws
3. T Sengupta, 2011, Intellectual Property Law in India, Kluwer Law International
4. Tzen Wong and Graham Dutfield, 2010, Intellectual Property and Human Development: Current Trends
and Future Scenario, Cambridge University Press
5. Cornish, William Rodolph & Llewelyn, David. 2010, Intellectual Property: Patents, Copyrights, Trade
Marks and Allied Right, 7th Edition, Sweet & Maxwell
6. Lous Harns, 2012, The enforcement of Intellactual Property Rights: A Case Book, 3rd Edition, WIPO
7. Prabhuddha Ganguli, 2012, Intellectual Property Rights, 1st Edition, TMH
8. R Radha Krishnan & S Balasubramanian, 2012, Intellectual Property Rights, 1st Edition, Excel Books
9. M Ashok Kumar and mohd Iqbal Ali, 2-11, Intellectual Property Rights, 2nd Edition, Serial Publications
10. Kompal Bansal and Praishit Bansal, 2012, Fundamentals of IPR for Engineers, 1st Edition, BS
Publications
11. Entrepreneurship Development and IPR Unit, BITS Pilani, 2007, A Manual on Intellectual Property
Rights,
12. Mathew Y Maa, 2009, Fundamentals of Patenting and Licensing for Scientists and Engineers, World
Scientific Publishing Company
13. N S Rathore, S M Mathur, Priti Mathur, Anshul Rathi , IPR: Drafting,Interpretation of Patent
Specifications and Claims , New India Publishing Agency
14. Vivien Irish, 2005, Intellectual Property Rights for Engineers,IET
15. Howard B Rockman, 2004, Intellectual Property Law for Engineers and scientists, Wiley -IEEE Press
Assessment :

Internal:
Assessment consists of two tests out of which; one should be compulsory class test and the other is either a
class test or at least 6 assignment on complete syllabus or course project.

End Semester Theory Examination:
Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question papers
of end semester examination. In question paper weightage of each module will be proportional to number of
respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then part (b)
will be from any module other than module 3)
4. Only Four question need to be solved.

Page 122

Course Code Course Name Credits
ILO8028 Digital Business Management 03

Objectives:
1. To familiarize with digital business concept
2. To acquaint with E-commerce
3. To give insights into E-business and its strategies

Outcomes: The learner will be able to …..
1. Identify drivers of digital business
2. Illustrate various approaches and techniques for E-business and management
3. Prepare E-business plan

Module Detailed content Hours





1 Introduction to Digital Business -

Introduction, Background and current status, E-market places, structures,
mechanisms, economics and impacts
Difference between physical economy and digital economy,

Drivers of digital business - Big Data & Analytics, Mobile, Cloud Computing,
Social media, BYOD, and Internet of Things(digitally intelligent machines/services)
Opportunities and Challenges in Digital Business,




09








2 Overview of E-Commerce

E-Commerce - Meaning, Retailing in e-commerce -products and services, consumer
behavior, market research and advertisement
B2B-E-commerce -selling and buying in private e -markets, public B2B exchanges
and support services, e -supply chains, Collaborative Commerce, Intra business EC
and Corporate portals
Other E -C models and applications, innovative EC System -From E - government
and learning to C2C, mobile commerce and pervasive computing
EC Strategy and Implementation -EC strategy and global EC, Economics and
Justification of EC, Using Affiliate marketing to promote your e- commerce
business, Launching a successful online business and EC project, Legal, Ethics and
Societal impacts of EC







06

Page 123

3 Digital Business Support services : ERP as e –business backbone, knowledge
Tope Apps, Information and referral system
Application Development: Building Digital business Applications and
Infrastructure
06




4 Managing E-Business -Managing Knowledge, Management skills for e -
business, Managing Risks in e –business Security Threats to e -business
-Security Overview, Electronic Commerce Threats, Encryption,
Cryptography, Public Key and Private Key Cryptography, Digital
Signa tures, Digital Certificates, Security Protocols over Public
Networks: HTTP, SSL, Firewall as Security Control, Public Key
Infrastructure (PKI) for Security, Prominent Cryptographic
Applications



06


5 E-Business Strategy -E-business Strategic formulation - Analysis of Company’s
Internal and external environment, Selection of strategy, E- business strategy into
Action, challenges and E-Transition (Process of Digital Transformation)

04
6 Materializing e -business : From Idea to Realization -Business plan preparation
Case Studies and presentations
08


References:

1. A textbook on E -commerce , Er Arunrajan Mishra, Dr W K Sarwade,Neha Publishers & Distributors,
2011
2. E-commerce from vision to fulfilment, Elias M. Awad, PHI-Restricted, 2002
3. Digital Business and E-Commerce Management, 6th Ed, Dave Chaffey, Pearson, August 2014
4. Introduction to E-business -Management and Strategy, Colin Combe, ELSVIER, 2006
5. Digital Business Concepts and Strategy, Eloise Coupey, 2nd Edition, Pearson
6. Trend and Challenges in Digital Business Innovation, VinocenzoMorabito, Springer
7. Digital Business Discourse Erika Darics, April 2015, Palgrave Macmillan
8. E-Governance -Challenges and Opportunities in : Proceedings in 2nd International Conference theory and
practice of Electronic Governance
9. Perspectives the Digital Enterprise –A framework for Transformation, TCS consulting journal Vol.5
10. Measuring Digital Economy -A new perspective -DOI: 10.1787/9789264221796 -enOECD Publishing


Assessment :

Internal:
Assessment consists of two tests out of which; one should be compulsory class test and the other is either a
class test or at least 6 assignment on complete syllabus or course project.

Page 124

End Semester Theory Examination:
Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question papers
of end semester examination. In question paper weightage of each module will be proportional to number of
respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then part (b)
will be from any module other than module 3)
4. Only Four question need to be solved.

Page 125

Course Code Course Name Credits
ILO8029 Environmental Management 03


Objectives:
1. Understand and identify environmental issues relevant to India and global concerns
2. Learn concepts of ecology
3. Familiarise environment related legislations

Outcomes: Learner will be able to…
1. Understand the concept of environmental management
2. Understand ecosystem and interdependence, food chain etc.
3. Understand and interpret environment related legislations



Module
Detailed Contents
Hrs

01 Introduction and Definition of Environment: Significance of Environment
Management for contemporary managers, Career opportunities.
Environmental issues relevant to India, Sustainable Development, The Energy
scenario.
10

02 Global Environmental concerns : Global Warming, Acid Rain, Ozone Depletion,
Hazardous Wastes, Endangered life-species, Loss of Biodiversity, Industrial/Man -made
disasters, Atomic/Biomedical hazards, etc.
06

03 Concepts of Ecology: Ecosystems and interdependence between living
organisms, habitats, limiting factors, carrying capacity, food chain, etc. 05

04 Scope of Environment Management, Role & functions of Government as a planning
and regulating agency.
Environment Quality Management and Corporate Environmental Responsibility
10
05 Total Quality Environmental Management, ISO-14000, EMS certification. 05

06 General overview of major legislations like Environment Protection Act, Air (P & CP)
Act, Water (P & CP) Act, Wildlife Protection Act, Forest Act, Factories Act, etc.
03


REFERENCES:

1. Environmental Management: Principles and Practice, C J Barrow, Routledge Publishers
London, 1999
2. A Handbook of Environmental Management Edited by Jon C. Lovett and David G. Ockwell, Edward
Elgar Publishing
3. Environmental Management, T V Ramachandra and Vijay Kulkarni, TERI Press
4. Indian Standard Environmental Management Systems — Requirements With Guidance For Use,
Bureau Of Indian Standards, February 2005
5. Environmental Management: An Indian Perspective, S N Chary and Vinod Vyasulu, Maclillan India,
2000

Page 126

6. Introduction to Environmental Management, Mary K Theodore and Louise Theodore,
CRC Press
7. Environment and Ecology, Majid Hussain, 3rd Ed. Access Publishing.2015



Assessment :

Internal:
Assessment consists of two tests out of which; one should be compulsory class test and the other
is either a class test or assignment on live problems or course project.

End Semester Theory Examination:
Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in
question papers of end semester examination. In question paper weightage of each module will be
proportional to number of respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3
then part (b) will be from any module other than module 3)
4. Only Four question need to be solved.

Page 127

Course Code: Course Title Credit
CSL801 Advanced AI Lab 01


Prerequisite: C/C++/Java/MATLAB
Lab Objectives:
1 Articulate basic knowledge of fuzzy set theory through programing.
2 To design Associative Memory Networks.
3 To apply Unsupervised learning towards Networks design.
4 To demonstrate Special networks and its applications in soft computing.
5 To implement Hybrid computing systems.
Lab Outcomes: At the end of the course, the students will be able to
1 Implement Fuzzy operations and functions towards Fuzzy -rule creations.
2 Build and training Associative Memory Network.
3 Build Unsupervised learning based networks .
4 Design and implement architecture of Special Networks
5 Implement Neuro -Fuzzy hybrid computing applications.

Suggested Experiments:
Sr. No. Name of the Experiment
1 Design and implement a Hidden Markov Models for outcome prediction.
2 Design and implement a Bayesian Network for outcome prediction.
3 Design and implement a Gaussian Mixture Models for outcome prediction.
4 Build and Train a Generative Multi -Layer Network Model using appropriate dataset.
5 Build and Train a Deep Convolution Generative Multi -Layer (DCGAN) Network Model
for an image based dataset.
6 Develop a Conditional GAN (CGAN) Network to direct the image generation process of
the generator model.
7 Train a variational autoencoder using Tensorflow on Fashion MNIST

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8 Explore the working of any pre-trained model towards outcome generation.
9 Implement and analyze the working of Local Interpretable Model -agnostic
Explanations(LIME) supervised model.
10 Case -study on the emerging technologies in AI like Metaverse, Augmented reality etc.
11 Mini Project Report: For any one chosen real world application as per the syllabus of
CSC801 : Advanced AI.
12 Implementation and Presentation of Mini Project


Useful Links
1 https://nptel.ac.in/courses/106106224
2 https://www.tensorflow.org/tutorials/generative/cvae
3 https://www.analyticsvidhya.com/blog/2022/07/everything -you-need -to-know -about -lime/
4 https://onlinecourses.nptel.ac.in/noc20_cs62/preview
5 https://machinelearningmastery.com/what -are-generative -adversarial -networks -gans/


Term Work:
1 Term work should consist of any 06 experiments, 1 case study, Mini Project.
2 Journal must include at least 2 assignments based on Theory and Practical’s.
3 The final certification and acceptance of term work ensures satisfactory performance of laboratory
work and minimum passing marks in term work.
4 Total 25 Marks (Experiments: 15-marks, Attendance Theory & Practical: 05-marks,
Assignments: 05-marks)
Practical and Oral exam
Oral examination on the entire syllabus of CSC801 and CSL801

Page 129

Lab Code Lab Name Credit
CSDOL8011 AI for financial & Banking
application Lab 1


Prerequisite: Python Programming, Deep Learning, Machine Learning.

Lab Objectives: Students will try
1 To implement digital money transfer systems in the banking sector.
2 To calculate risk-adjusted performance measures for investment portfolios.
3 To apply cluster analysis to identify patterns in financial data.
4 To analyze market sentiment using the Markov regime switching model.
5 To design and backtest trading algorithms for financial markets
6 To detect and prevent fraudulent activities using fraud analytics techniques

Lab Outcomes: At the end of the course, the students will be able to
1 Proficiency in implementing secure and efficient digital money transfer systems.
2 Ability to assess investment performance using risk-adjusted measures.
3 Competence in identifying meaningful patterns and segments in financial data.
4 Understanding of market sentiment and its impact on trading decisions.
5 Practical skills in developing and evaluating trading algorithms.
6 Knowledge of fraud detection methods for financial systems.


Suggested List of Experiments
1. Setting up a Digital Money Transfer System
2. Calculating Sharpe Ratios for Investment Portfolios
3, Cluster Analysis of Financial Data for Market Segmentation
4. Analyzing Market Sentiment using the Markov Regime Switching Model
5. Developing and Backtesting a Simple Trading Algorithm
6. Implementing Advanced Risk Management Techniques in Trading Algorithms
7. Fraud Detection using Machine Learning Algorithms
8. Visualizing Fraud Patterns and Analytics
9. Designing and Backtesting Complex Trading Strategies
10. Evaluating and Enhancing the Performance of Trading Algorithms
11. Applying Machine Learning for Predictive Fraud Analytics

Page 130

Textbooks:
1 Financial Analytics with R Building a Laptop Laboratory for Data Science MARK J.
BENNETT University of Chicago DIRK L. HUGEN University of Iowa
2 Artificial Intelligence in Finance A Python -Based Guide, Yves Hilpisch A
3 Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A
Guide to Data Science for Fraud Detection , Bart Baesens, Veronique Van Vlasselaer,
Wouter Verbeke

References:
1 “ Machine Learning for Asset Managers" by Marcos López de Prado
2 "Advances in Financial Machine Learning" by Marcos López de Prado.

Digital References:
1. https: //www.eastnets.com/newsroom/digital -transformation -in-the-banking -and-financial -services -sector
2. https://www.techopedia.com/definition/34633/generative -ai



Term Work:
1 Term work should consist of 10 experiments and 2 assignments.
2 The final certification and acceptance of term work ensures satisfactory performance of
laboratory work and minimum passing marks in term work.
3 The final certification and acceptance of term work ensures satisfactory performance of
laboratory work and minimum passing marks in term work. Total 25 Marks (Experiments and
Project: 15-marks, Attendance(Theory & Practical): 05 -marks, Assignment: 05 -marks)
Practical and Oral exam
Oral examination on the entire syllabus of CSDO8011 & CSDOL8011

Page 131

Lab Code Lab Name Credit
CSDOL8012 Quantum Computing Lab 1

Prerequisite: Python Programming Language.
Lab Objectives:
1 To implement fundamental quantum computing concepts
2 To learn quantum computation and quantum information
3 To understand quantum entanglement, quantum algorithms
4 To understand quantum information theory and channels
Lab Outcomes: Students will be able to
1 Implement basic quantum computing logic by building dice and random numbers using open
source simulation tools.
2 Understand quantum logic gates using open source simulation tools.
3 Implement quantum circuits using open source simulation tools.
4 I implement quantum algorithms using open source simulation tools.

Suggested Experiments: Students are required to complete at least 10 experiments.Faculty may
develop their own set of experiments for students. List below is only suggestive.
Sr. No. Name of the Experiment
1 Building Quantum dice
2 Building Quantum Random No. Generation
3 Composing simple quantum circuits with q-gates and measuring the output into
classical bits.
4 Implementation of Shor‘s Algorithms
5 Implementation of Grover‘s Algorithm
6 Implementation of Deutsch‘s Algorithm
7 Implementation of Deutsch -Jozsa‘s Algorithm
8 Quantum Circuits
9 Qubit Gates
10 Bell Circuit & GHZ Circuit
11 Accuracy of Quantum Phase Estimation
12 Mini Project such as implementing an API for efficient search using Grover‘s
Algorithms or Integer factorization using Shor‘s Algorithm.

Useful Links:
1 IBM Experience: https://quantum -computing.ibm.com/
2 Microsoft Quantum Development Kit
https://azure.microsoft.com/en -us/resources/development -kit/quantum -compu ting/#overview
3 Forest SDK PyQuil: https://pyquil -docs.rigetti.com/en/stable/
4 Google Quantum CIRQ https://quantumai.google/cirq
5 Qiskit Labs IBM https://learn.qiskit.org/course/ch -labs/lab -1-quantum -circuits

Page 132

Term Work:
1 Term work should consist of 10 experiments.
2 Journal must include at least 2 assignments.
3 The final certification and acceptance of term work ensures that satisfactory performance of
laboratory work and minimum passing marks in term work.
4 Total 25 Marks (Experiments: 15 -marks, Attendance Theory & Practical: 05 -marks,
Assignments: 05-marks)
Oral & Practical exam:
Oral examination based on the entire syllabus of CSDO8012 and CSDOL8012

Page 133

Course Code: Course Title Credit
CSDOL8013 Reinforcement Learning Lab 1


Prerequisite: Python Programming, Deep Learning, Machine Learning.

Lab Objectives: Students will try
1 Introduce the fundamentals of reinforcement learning and problem formulation using MDPs
and Bandit problems
2 Explode different exploration strategies and their impact on online leaning scenarios.
3 Understand dynamic programming algorithms for solving Markov Decision Processes.
4 Apply dynamic programming techniques to solve small -scale MDP problems
5 Implement and compare Monte Carlo methods and Temporal -Difference learning algorithms.
6 Explore real-world applications of reinforcement learning in domains such as autonomous
driving or robotics

Lab Outcomes: At the end of the course, the students will be able to
1 Gain a solid understanding of reinforcement learning concepts and problem formulation.
2 Evaluate and compare exploration strategies in online learning scenarios.
3 Solve Markov Decision Processes using dynamic programming algorithms
4 Apply dynamic programming techniques to solve small -scale MDP problems.
5 Implement and analyze Monte Carlo methods and Temporal -Difference learning algorithms
6 Explore practical applications of reinforcement learning in real-world domains.


Suggested List of Experiments
1. Implementing a simple grid-world environment and training an agent using basic Q-
learning
2. Implementing a multi -armed bandit problem and comparing different exploration strategies
like epsilon -greedy and UCB.
3, Implementing a basic grid -world environment as an MDP and applying policy iteration
and value iteration algorithms to find optimal policies.
4. Applying dynamic programming algorithms, such as policy evaluation and policy
improvement, to solve a small -scale MDP problem.
5. Implementing Monte Carlo control and Temporal Difference (TD) learning algorithms to
train an agent in a grid-world environment.
6. Exploration vs. Exploitation Trade -off: Experimenting with different exploration strategies
and analyzing their impact on the learning performance of an agent in a bandit problem.
7. Function Approximation in Reinforcement Learning: Using function approximation

Page 134

techniques, such as linear regression or neural networks, to approximate value functions in
reinforcement learning problems.
8. Deep Reinforcement Learning: Implementing a deep Q-network (DQN) to train an agent to
play a popular Atari game, such as Pong or Space Invaders.
9. Transfer Learning and Multi -Task Reinforcement Learning: Investigating transfer learning
in reinforcement learning by training an agent in one environment and transferring its
knowledge to a different but related environment
10. Policy Gradient Methods:
Implementing policy gradient methods, such as REINFORCE or Proximal Policy
Optimization (PPO), to train an agent in a continuous control environment.
*11. Applications and Case Studies:
Applying reinforcement learning techniques to solve a real-world problem, such as training
a self-driving car to navigate a simulated road environment.


Text Books:

1. Reinforcement Learning: An Introduction, by Richard S. Sutton and Andrew G. Barto
2. Alessandro Palmas, Dr. Alexandra Galina Petre, Emanuele Ghelfi, The Reinforcement
Learning Workshop: Learn how to Apply Cutting -edge Reinforcement Learning Algorithms
to a Wide Range of Control Problems, 2020 Packt publishing.
3. Phil Winder , Reinforcement Learning Industrial Applications with Intelligent Agents, O’Reilly
4. Dr Engr S M Farrukh Akhtar, Practical Reinforcement Learning, Packt Publishing, 2017.

References Books:

1. Maxim Lapan, Deep Reinforcement Learning Hands -On: Apply modern RL methods, with
deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero.
2. Csaba Szepesv´ari, Algorithms for Reinforcement Learning, Morgan & Claypool Publishers
3. Alberto Leon -Garcia, Probability, Statistics and Random Processes for Electrical
Engineering, Third Edition, Pearson Education, Inc.

Useful Links
1. Machine Learning and Friends at Carnegie Mellon University
2. Reinforcement Learning: A Survey
3. Bibliography on Reinforcement Learning
4. David J. Finton's Reinforcement Learning Page



Term Work:
1 Term work should consist of any 8 experiments, 1 case study and 2 assignments.
2 The final certification and acceptance of term work ensures satisfactory performance o

Page 135

laboratory work and minimum passing marks in term work.
3 The final certification and acceptance of term work ensures satisfactory performance of
laboratory work and minimum passing marks in term work. Total 25 Marks (Experiments and
Project: 15-marks, Attendance(Theory & Practical): 05-marks, Assignment: 05-marks)

Oral exam
Oral Examination based on the entire syllabus of CSDO8011 and CSL8011

Page 136

Lab Code Lab Name Credit
CSDOL8021 Graph Data Science
Lab 1



Lab Objectives: Students will try
1 To understand graph database fundamentals and their advantages.
2 To design and implement effective data models using the labeled property graph model.
3 To develop proficiency in querying and analyzing graph data using Cypher.
4 To gain knowledge of graph database administration tasks and data management.
5 To apply graph database techniques to real-world use cases.
6 To develop practical skills in graph database application development.

Lab Outcomes: At the end of the course, the students will be able to
1 Comprehensive understanding of graph databases and their benefits.
2 Proficiency in creating data models for representing complex relationships.
3 Ability to write efficient queries and analyze graph data effectively.
4 Competence in administering and managing graph databases.
5 Application of graph database techniques to solve real-world problems.
6 Understand developing graph database applications.




Suggested List of Experiments
1. Graph Database Fundamentals:
○ Install and set up a graph database system (e.g., Neo4j) on a local machine.
○ Familiarize yourself with the graph database environment, including the
query language (Cypher) and browser interface. Prerequisite: Python Programming, Deep Learning, Machine Learning.

Page 137

2. Data Modeling with Graphs:
○ Design a data model using the labeled property graph model for a specific
domain (e.g., social network, e-commerce).
○ Implement the data model in the graph database and populate it with sample
data.
3, Basic Graph Queries:
○ Perform basic graph queries using Cypher to retrieve nodes, relationships,
and their properties.
○ Explore different query patterns, such as finding paths, filtering nodes, and
ordering results.
4. Advanced Graph Queries:
○ Extend your query knowledge by performing more complex graph queries,
including subgraph matching, aggregation, and conditional filtering.
○ Optimize query performance by understanding and utilizing indexes.
5. Graph Database Administration:
○ Learn and practice essential administrative tasks, such as managing users,
roles, and access control.
○ Perform backup and restore operations to ensure data integrity.
6. Importing and Exporting Data:
○ Import data from external sources (e.g., CSV files) into the graph database.
○ Export graph data to different formats for analysis or sharing.
7. Graph Algorithms and Analytics:
○ Explore the built-in graph algorithms provided by the graph database system
(e.g., centrality, community detection).
○ Apply graph algorithms to analyze and extract insights from your graph data
8. Graph Visualization and Exploration:
○ Utilize visualization tools and libraries to visualize your graph data.
○ Explore and navigate the graph visually to gain a better understanding of its
structure and relationships.
9. Performance Optimization:
○ Identify and address performance bottlenecks in your graph database
application.
○ Optimize queries, indexes, and data modeling to improve overall system

Page 138

performance.
10. Scaling and Replication:
○ Learn techniques for scaling and replicating a graph database to handle
larger datasets and higher workloads.
○ Implement and test replication strategies to ensure data availability and fault
tolerance.
*11. Real-World Use Cases:
○ Choose a specific real-world use case (e.g., recommendation systems, fraud
detection) and apply graph database techniques to solve the problem.
○ Design and implement a graph database application that addresses the unique
requirements of the chosen use case.



Textbooks:
1 Introduction to Graph Theory Fourth edition, Robin J. Wilson
2 Daphne Koller and Nir Friedman, "Probabilistic Graphical Models: Principles and
Techniques”, Cambridge, MA: The MIT Press, 2009 (ISBN 978-0-262-0139 - 2).
3 Graph databases, Ian Robinson, Jim Webber & Emil Eifrem

References:
1
"Graph Databases: New Opportunities for Connected Data" by Ian Robinson, Jim
Webber, and Emil Eifrém.
2 "Neo4j in Action" by Aleksa Vukotic, Nicki Watt, and Tareq Abedrabbo.
3 "Graph Databases for Beginners" by Mark Needham and Amy E. Hodler.
4 "Practical Neo4j" by Gregory Jordan.
5 "Learning Neo4j" by Rik Van Bruggen.
6 "Graph Database Applications and Concepts with Neo4j" by Dionysios Synodinos.

Digital References:

Page 139

Term Work:
1 Term work should consist of any 8 experiments , 1 case study and 2 assignments.
2 The final certification and acceptance of term work ensures satisfactory performance of
laboratory work and minimum passing marks in term work.
3 The final certification and acceptance of term work ensures satisfactory performance of
laboratory work and minimum passing marks in term work. Total 25 Marks (Experiments and
Project: 15-marks, Attendance(Theory & Practical): 05 -marks, Assignment: 05 -marks)

Oral exam
Oral examination on the entire syllabus of CSDO8021 and CSDOL8021
2. https://www.quackit.com/neo4j/tutorial/
1. https://web4.ensiie.fr/~stefania.dumbrava/OReilly_Graph_Databases.pdf

Page 140

Course Code: Course Title Credit
CSDOL8022 Recommendation Systems Lab 1


Prerequisite: Java/Python
Lab Objectives:
1 To understand the key concepts of Recommendation systems.
2 Design and implement cluster -based approaches for recommendation systems.
3 Design, implement and analyze classification algorithms for recommendation systems.
4 To understand various Recommendation system Algorithms.
5 To understand data processing for Recommendation system Algorithms
Lab Outcomes: At the end of the course, the students will be able to
1 Understand mathematics and representation of data for recommendation systems.
2 Design, implement and analyze Collaborative filtering based for recommendation systems.
3 Design, implement and analyze Content -based recommendation systems.
4 Design, implement and analyze Knowledge -based recommendation systems.
5 Understanding feature engineering and pre-processing for recommendation systems.
6 To solve real world problems using recommendation systems.

Suggested Experiments:
Sr. No. Name of the Experiment
1 Implementation of Matrix operations and data representation towards understanding
mathematics for recommendation system
2 Experiment on the role of clustering methods with respect to recommendation systems
3 Feature engineering and pre -processing of data for recommendation systems.
4 Implementation of Bayes classifier for recommendation.
5 Implement User -based Nearest neighbor recommendation.
6 Implement Item-based Nearest neighbor recommendation
7 Implement Content -based recommendation system.
8 Implement Knowledge -based recommendation system.

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9 Implementation of a recommendation system using Hybrid approach.
10 Implementation of a recommendation system using Ensembled approach.
11 Implementation of a Regression based recommendation system.
12 Analyze results on the basis of different evaluation parameters and graphical
representations for recommendation systems.
13 Mini Project Report: For any one chosen real world Recommendation systems
application.
14 Implementation and Presentation of Mini Project

Useful Links
1 https://towardsdatascience.com/recommendation -systems -explained -a42fc60591ed
2 https://www.coursera.org/specializations/recommender -systems


Term Work:
1 Term work should consist of any 08 experiments and mini project
2 Journal must include at least 2 assignments based on Theory and Practical’s
3 The final certification and acceptance of term work ensures satisfactory performance of
laboratory work and minimum passing marks in term work.
4 Total 25 Marks (Experiments: 15-marks, Attendance Theory & Practical: 05-marks,
Assignments: 05-marks)
Oral exam:
Oral examination based on the entire syllabus of CSDO8022 and CSL8022

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Lab Code Lab Name Credit
CSDOL8023 Social Media Analytics Lab 1


Prerequisite : Types of Graphs, Data Mining, Data Analytics
Lab Objectives:
1 To understand the fundamental concepts of social media networks.
2 To learn various social media analytics tools and evaluation matrices.
3 To collect and store social media data.
4 To analyze and visualize social media data
5 To design and develop social media analytics models.
6 To design and build a social media analytics application.
Lab Outcomes: The students will be able to
1 Understand characteristics and types of social media networks.
2 Use social media analytics tools for business
3 Collect, monitor , store and track social media data
4 Analyze and visualize social media data from multiple platforms
5 Design and develop content and structure based social media analytics models.
6. Design and implement social media analytics applications for business.


Suggested Experiments:

Sr. No. Name of the Experiment
1 Study various -
i) Social Media platforms ( Facebook, twitter, YouTubeetc)
ii) Social Media analytics tools ( Facebook insights, google analytics
net lyticetc)
iii) Social Media Analytics techniques and engagement metrics (page level,
post level, member level)
iv) Applications of Social media analytics for business.
e.g. Google Analytics
https://marketingplatform.google.com/about/analytics /

https://netlytic.org/
2 Data Collection -Select the social media platforms of your choice (Twitter,
Facebook, LinkedIn, YouTube, Web blogs etc) ,connect to and capture social media
data for business ( scraping, crawling, parsing).

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3 Data Cleaning and Storage - Preprocess, filter and store social media data for
business (Using Python, MongoDB, R, etc).
4 Exploratory Data Analysis and visualizationof Social Media Data for business.
5 Develop Content (text, emoticons, image, audio, video) based social media
analytics model for business.
(e.g. Content Based Analysis :Topic , Issue ,Trend, sentiment/opinion analysis,
audio, video, image analytics)
6 Develop Structure based social media analytics model for any business.
( e.g. Structure Based Models -community detection, influence analysis)
7 Develop a dashboard and reporting tool based on real time social media data.
8 Design the creative content for promotion of your business on social media

platform.
9 Analyze competitor activities using social media data.
10 Develop social media text analytics models for improving existing product/ service
by analyzing customer‘s reviews/comments.

Reference Books:

1 Python Social Media Analytics: Analyze and visualize data from Twitter, YouTube,
GitHub, and more Kindle Edition by Siddhartha Chatterjee , Michal Krystyanczuk
2 Learning Social Media Analytics with R,byRaghav Bali, Dipanjan Sarkar, Tushar
Sharma.
3 Jennifer Golbeck, Analyzing the social web, Morgan Kaufmann, 2013
4 Matthew A. Russell. Mining the Social Web: Data Mining Facebook, Twitter,
Linkedin, Google+, Github, and More, 2nd Edition, O'Reilly Media, 2013
5 Charu Aggarwal (ed.), Social Network Data Analytics, Springer, 2011

Term Work:
1 Term work should consist of 10 experiments.
2 Journal must include at least 2 assignments.
3 The final certification and acceptance of term work ensures satisfactory performance
of laboratory work and minimum passing marks in term work.
4 Total 25 Marks (Experiments: 15-marks, Attendance Theory & Practical: 05-marks,
Assignments: 05-marks)
Practical and Oral Exam
Oral examination based on the entire syllabus of CSDC8023 and CSDL80223

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Course Code: Course Title Credit
CSP801 Major Project 2 6


Course Objectives:
1 To acquaint with the process of identifying the needs and converting it into the problem.
2 To familiarize the process of solving the problem in a group.
3 To acquaint with the process of applying basic engineering fundamentals to attempt solutions to the
problems.
4 To inculcate the process of self-learning and research.
Course Outcomes:
1 Identify problems based on societal /research needs.
2 Apply Knowledge and skill to solve societal problems in a group
3 Draw the proper inferences from available results through theoretical/ experimental/simulations
4 Analyse the impact of solutions in societal and environmental context for sustainable development.
5 Demonstrate capabilities of self-learning in a group, which leads to lifelong learning.
6 Demonstrate project management principles during project work.

Guidelines:
1. Internal guide has to keep track of the progress of the project and also has to maintainattendance
report. This progress report can be used for awarding term work marks.

2. Project Report Format:
At the end of semester, each group needs to prepare a project report as per the guidelines issued by the
University of Mumbai. Report should be submitted in hardcopy. Also, each group should submit
softcopy of the report along with project document ation, implementation code, required utilities,
software and user Manuals.

A project report should preferably contain at least following details:
o Abstract
o Introduction
o Literature Survey/ Existing system
o Limitation Existing system or research gap
o Problem Statement and Objective
o Proposed System
o Analysis/Framework/ Algorithm
o Design details
o Methodology (your approach to solve the problem) Proposed System
o Experimental Set up

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o Details of Database or details about input to systems or selected data
o Performance Evaluation Parameters (for Validation)
o Software and Hardware Setup
o Results and Discussion
o Conclusion and Future Work
o References
o Appendix – List of Publications or certificates

Desirable:
Students should be encouraged -
o to participate in various project competition.
o to write minimum one technical paper & publish in good journal.
o to participate in national / international conference.
3. Term Work:

Distribution of marks for term work shall be done based on following:
a. Weekly Log Report
b. Completeness of the project and Project Work Contribution
c. Project Report (Black Book) (both side print)
d. Term End Presentation (Internal)

The final certification and acceptance of TW ensures the satisfactory performance on
the above aspects.

4. Oral & Practical:

Oral &Practical examination (Final Project Evaluation) of Project 2 should be conducted by
Internal and External examiners approved by University of Mumbai at the end of the semester.

Suggested quality evaluation parameters are as following:
a. Relevance to the specialization / industrial trends
b. Modern tools used
c. Innovation
d. Quality of work and completeness of the project
e. Validation of results
f. Impact and business value
g. Quality of written and oral presentation
h. Individual as well as teamwork