R MSC CS Syllabus Sem III IV CBCS REVISED 2022 23 2 1 Syllabus Mumbai University by munotes
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AC – 11/7/2022
Item No. – 6.7 (R)
University of Mumbai
Revised Syllabus for
Masters of Science (Computer Science)
Semester – III & IV
(Choice Based Credit System)
(With effect from the academic year 2022 -23)
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CONTENTS
1. PREAMBLE
2. PROGRAMME STRUCTURE
3. DETAILED SYLLABUS FOR SEMESTER - III & SEMESTER – IV
4. EVALUATION
5. SCHEME OF EXAMINATION AND DISTRIBUTION PATTERN OF MARKS
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1. PREAMBLE
This syllabus is an extension of the syllabus for the semester - I and semester – II of M.Sc.
Computer Science of the University of Mumbai, which came into existence in the academic
year 2021 -2022. As mentioned in the syllabus forthe semester I and II, the intended philosophy
of the new syllabus is to meet the following guidelines:
● To be fundamentally strong at the core subject of Computer Science.
● To apply programming, computational skills, and the latest technological trends for
industrial solutions.
● Offer specialization in a chosen area.
● Create research temper among students in the whole process.
● To encourage, motivate and prepare the Learners for Lifelong - learning.
● To inculcate professional and ethical attitude, good leadership qualities, and
commitm ent to social responsibilities in the Learner’s thought process.
This syllabus for the semester - III and semester – IV has tried to continue the steps initiated in
the semester - I and semester –II to meet the goals set. This proposes Four Tracks in semes ter-
III. The student must select one paper from each track.
The Four Elective in semester - III is mentioned below:
● Data Science
● Advanced Computing
● Security
● Computer Networking
Semester - IV will have two papers which shall be conducted in ONLINE MODE. A ratio of
70:30 needs to be followed i.e. 70% percent of lectures and practicals shall be in ONLINE
MODE and the remaining 30% of lectures and practicals will be conducted purely i n OFFLINE
MODE.
The syllabus also offers an internship with industry and project implementation in semester IV,
each of which has weights equivalent to a full course. Introducing different Electives in Tracks
in semester –III and offering the opportunity to choose those Electives will give the student
added advantage of high -level competency in the advanced and emerging areas of computer
science. This will equip the student with industry readiness as an internship in an IT or IT -
related organization gives a practical exposure to what is learned and what is practiced. The
strong foundation given in the core courses in different semesters will give enough confidence
to the learner to face and adapt to the changing trends and requirements of industry and
acade mia.
The syllabus prepares a strong army of budding computer science researchers. The syllabus
was designed on the firm belief that focusing on student -driven research on cutting edge and
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emerging trends with lots of practical experience will make the le arning more interesting and
stimulating. It is hoped that the student community and teacher colleagues will appreciate the
thrust, direction, and treatment given in the syllabus.
We thank all our colleagues at the University of Mumbai for their inputs, suggestions, and
critical observations. We acknowledge the contributions of experts from premier institutions
and industry for making the syllabus more relevant. We thank the Chairperson and Members
of the present Ad -hoc Board of Studies in Computer Scienc e of the University for their constant
support. Thanks to one and all who have directly or indirectly helped in this venture.
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2. PROGRAMME STRUCTURE
SEMESTER - III
The syllabus proposes Four Tracks with Two Electives each in semester -III with theory and
practical components. Each of these courses is of Four Credits and Two credits respectively.
The following table gives the details of the Theory Courses in Semeste r -III.
Semester - III: Theory courses
Course
Code Course Title No of
Hours Credit
Track -A: Advanced Computing
Select Any one from the courses listed below along with corresponding practical course
PSCS3011 Elective -1: Advanced Computing (Web3 Technologies)
60 04
PSCS3012 Elective -2: Advanced Computing (Trends in Cloud
Computing)
Track -B: Security
Select Any one from the courses listed below along with corresponding practical course
PSCS3021 Elective -1: Security ( Cryptography and Cryptanalysis )
60 04
PSCS3022 Elective -2: Security (Cyber Security and Risk Assessment)
Track -C: Computer Networking
Select Any one from the courses listed below along with corresponding practical course
PSCS3031 Elective -1: Computer Networking (Server and Data Centric
Networking) 60 04
PSCS3032 Elective -2: Computer Networking (Wireless Networking)
Track -D: Data Science
Select Any one from the courses listed below along with corresponding practical course
PSCS3041 Elective -1: Data Science (Data Visualization)
60 04
PSCS3042 Elective -2: Data Science (Big Data Analytics)
Total Credits for Theory courses in Semester - III 240 16
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Semester - III: Practical Lab courses
The syllabus proposes Four laboratory courses of 02 Credits each. As far as the practical is
concerned, equal weightage similar to that of theory courses have been given in terms of the
number of hours.
The following table gives the details of the Practi cal Courses in Semester –III
Course
Code Course Title No of
Hours Credit
Track -A: Advanced Computing
Select Any one from the courses listed below along with corresponding practical course
PSCSP3011 Elective -1: Advanced Computing (Web3 Technologies)
60 02
PSCSP3012 Elective -2: Advanced Computing (Trends in Cloud
Computing)
Track -B: Security
Select Any one from the courses listed below along with corresponding practical course
PSCSP3021 Elective -1: Security ( Cryptography and Cryptanalysis )
60 02
PSCSP3022 Elective -2: Security (Cyber Security and Risk Assessment)
Track -C: Computer Networking
Select Any one from the courses listed below along with corresponding practical course
PSCSP3031 Elective -1: Computer Networking (Server and Data Centric
Networking) 60 02
PSCSP3032 Elective -2: Computer Networking (Wireless Networking)
Track -D: Data Science
Select Any one from the courses listed below along with corresponding practical course
PSCSP3041 Elective -1: Data Science (Data Visualization)
60 02
PSCSP3042 Elective -2: Data Science (Big Data Analytics)
Total Credits for Theory courses in Semester - III 240 08
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SEMESTER - IV
The syllabus proposes Two subjects in Semester – IV, and each subject has a Theory and
Practical components. In addition, there will be an Internship with Industry and Project
Implementation.
The following table gives the details of the Theory Courses i n Semester - IV.
Semester - IV: Theory courses
Course Code Course Title No of Hours Credit
PSCS401 Robotics (Online Mode) 60 04
PSCS402 Advanced Deep Learning (Online Mode) 60 04
Total Credits for Theory courses in Semester - IV 120 08
Semester - IV: Practical Lab courses
The syllabus proposes Two laboratory courses of 2 Credits each. As far as the practical is
concerned, equal weightage similar to that of Theory courses have been given in terms of the
number of hours.
The following table summa rizes the details of the Practical Courses in the Semester -IV
Course Code Course Title No of Hours Credit
PSCSP401 Robotics (Online Mode) 60 02
PSCSP402 Advanced Deep Learning (Online Mode) 60 02
Total Credits for Practical courses in Semester - IV 120 04
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Internship with industry
The syllabus proposes an internship for about 8 weeks to 12 weeks to be done by a student. It is
expected that a student chooses an IT or IT -related industry and formally works as a full -time
intern during the period. The student should subject himself/herselfto an internship evaluation
with proper documentation of the attendance and the type of work he or she has done in the
chosen organization. Proper certification (as per the guidelines given in Appendix 1 and 2) by
the person, to whom the student was reporting, with Organization’s seal should be attached as
part of the documentation.
Course Code Course Title No of Hours Credit
PSCSP403 Internship with Industry 300 06
Project Implementation
The syllabus proposes project implementation as part of the semester –IV. The student is expected
to submit the proposal and implement the same in the semester –IV. In addition, experimental
setup, analysis of results, comparison with results of related works, conclusion , and prospects
will be part of the project implementation. A student is expected to make a project
implementation report and appear for a project viva. He or she needs to spend around 200 hours
for the project implementation, which fetches 6 credits. The details are given below:
Course Code Course Title No of Hours Credit
PSCSP404 Project Implementation 200 06
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3. DETAILED SYLLABUS FOR SEMESTER - III & SEMESTER - IV
SEMESTER - III
Course Code Course Title Credits
PSCS3011 Elective -1: Advanced Computing (Web3 Technologies) 04
Course Outcome: -
● To cover the technical aspects of cryptocurrencies, blockchain technologies, and distributed
consensus.
● To familiarize potential applications for Bitcoin -like cryptocurrencies
● To Basics of smart contracts, decentralized apps, and decentralized anonymous organizations
(DAOs)
● To know Solidity programming
Course Specific Outcome: -
● Understand blockchain technology.
● Develop blockchain -based solutions and write smart contracts using Hyperledger Fabric and
Ethereum frameworks.
● Build and deploy blockchain applications for on -premise and cloud -based architecture.
● Integrate ideas from various domains and implement them us ing blockchain technology from
different perspectives.
UNIT 1: Introduction to Web3 Technologies
Blockchain: Growth of blockchain technology, Distributed systems , the history of
blockchain and Bitcoin, Blockchain , Consensus, CAP theorem and blockchain,
Decentralization using blockchain,Methods of decentralization,Routes to decentralization,
Blockchain and full ecosystem decentralization, The consensus problem, Analysis and
design, Classification, Algorithms,
Bitco in: Overview, Cryptographic keys, Transactions, Blockchain Mining, Bitcoin network,
Wallets, Bitcoin payments, Innovation in Bitcoin, Advanced protocols, Bitcoin investment,
and buying and selling Bitcoin
15L
UNIT 2: Smart Contracts & Ethereum
Smart Contracts: History, Definition Ricardian contracts, Smart contract templates,
Oracles, Deploying smart contracts, The DAO
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Ethereum: Overview, Ethereum network, Components of the Ethereum ecosystem, The
Ethereum Virtual Machine (EVM), Smart contracts , Blocks and Blockchain, Wallets and
client software, Nodes and miners, APIs, tools, and DApps, Supporting protocols,
Programming languages,
Ethereum Development Environment: Overview, Test networks, Components of a private
network, starting up the private network, mining on the private network, Remix IDE,
MetaMask, Using MetaMask and Remix IDE to deploy a smart contract
15L
UNIT 3: Serenity, Ethereum, Hyperledger & Tokenization
Web3: Exploring Web3 with Geth, Contract deployment, interacting with contracts via
frontends
Development frameworks: Serenity, Ethereum 2.0 —an overview, Development, phases,
Architecture
Serenity: Ethereum 2.0 —an overview, Development phases, Architecture
Hyperledger: Projects under Hyperledger, Hyperledger reference arch itecture, Hyperledger
Fabric, Hyperledger Sawtooth, Setting up a sawtooth development environment.
Tokenization: Tokenization on a blockchain, Types of tokens, Process of tokenization,
Token offerings, Token standards, Trading and finance, DeFi, Building a n ERC -20 token,
emerging concepts
15L
UNIT 4: Solidity Programming (Skill Enhancement)
Introduction to Solidity Programming: Layout of a Solidity Source File, Structure of a
Contract, Types, Units, and Globally Available Variables, Input Parameters and Output
Parameters, Control Structures, Function Calls, Creating Contracts via new, Order of
Evaluation of Expressions, Assignme nt, Scoping and Declarations, Error handling: Assert,
Require, Revert and Exceptions
Smart Contracts: Solidity Programming –Contracts, Creating Contracts, Visibility and
Getters, Function Modifiers, Constant State Variables, Functions, Inheritance, Abstrac t
Contracts, Interfaces, Libraries.
15L
TEXTBOOKS:
1. Mastering Blockchain: A deep dive into distributed ledgers, consensus protocols, smart
contracts, DApps, cryptocurrencies, Ethereum, and more, 3rd Edition 2020
2. Andreas M. Antonopoulos, Dr.Gavin wood “Mastering Ethereum” O‟Reilly Media Inc,
2019
3. Ritesh Modi, “Solidity Programming Essentials: A Beginner‟s Guide to Build Smart
Contracts for Ethereum and BlockChain”, Packt Publishing.
REFERENCE BOOKS:
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1. Josh Thompson, „Blockchain: The Blockchain fo r Beginnings, Guild to Blockchain
Technology and Blockchain Programming‟, Create Space Independent Publishing Platform,
First Edition - 2017.
Course Code Course Title Credits
PSCSP3011 Practical Course on Elective -1: Advanced Computing (Web3
Technologies) 02
Note: The following practical can be performed using Solidity, NodeJS. Ethereum and any
other suitable platform
1 Install and understand Docker container, Node.js, Java and Hyperledger Fabric, Ethereum
and perform necessary software inst allation on local machine/create instance on Cloud to
run.
2 Create and deploy a block chain network using Hyperledger Fabric SDK for Java
3 Interact with a block chain network. Execute transactions and requests against a block
chain network by creating an app to test the network and its rules
4 Deploy an asset -transfer app using block chain. Learn app development within a
Hyperledger Fabric network
5 Use block chain to track fitness club rewards
Build a web app that uses Hyperledger Fabric to track and trace member rewards
6 Car auction network: A Hello World example with Hyperledger Fabric Node SDK and
IBM Block chain Starter Plan. Use Hyperledger Fabric to invoke chaincode while storing
resultsand data in the starter plan
7 Develop an IoT asset tracking app using Block chain. Use an IoT asset tracking device to
improve a supply chain by using Block chain, IoT devices, and Node -RED
8 Create a global finance block chain application with IBM Block chain Platform
Extension for VS Code. Develop a Nod e.js smart contract and web app for a Global Finance
with block chain use case
9 Develop a voting application using Hyperledger and Ethereum. Build a decentralized
app that combines Ethereum's Web3 and Solidity smart contracts with Hyperledger's
hosting F abric and Chaincode EVM
10 Create a block chain app for loyalty points with Hyperledger Fabric Ethereum Virtual
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Machine. Deploy Fabric locally with EVM and create a proxy for interacting with a smart
contract through a Node.js web app
Course Code Course Title Credits
PSCS3012 Elective -2: Advanced Computing (Trends in Cloud Computing) 04
Course Outcome: -
● Learners will be able to develop and launch applications in the cloud environment
● Explore various frameworks and APIs that are used for developing cloud -based applications
● Handling data in a Cloud environment
Course Specific Outcome: -
● Design, develop & deploy real -world applications in the cloud computing platforms
● Demonstrate the ability to access the various cloud platforms
● Describe the standardization process of the cloud platform and various API’s used in Cloud
Computing
● Describe the methods for managing the data in the cloud
● Analyze and use of an appropriate framework and APIs for the task
● Design dashboards for management across cloud -based service
UNIT 1: Basic Concepts & Techniques for Cloud Application Development
Fundamentals of Cloud Application Development: Business case for implementing cloud
application, Requirements collection for cloud application development, Cloud service
models and deployment models, Open challenges in Cloud Computing: Cloud
interoperability and standards, scalability and fault tolerance, secu rity, trust, and privacy
Application Development framework: Accessing the clouds: Web application vs Cloud
Application, Frameworks: Model View Controller (MVC). Cloud platforms in Industry –
Google AppEngine, Microsoft Azure, Openshift, CloudFoundry
15L
UNIT 2: Cloud Service Delivery Environment and API
Sessions and API: Storing objects in the Cloud, Session management, Working with third
party APIs: Overview of interconnectivity in Cloud ecosystems. Facebook API, Twitter API,
Google API.
Architecting for the Cloud: Best practices in architecture cloud applications in AWS cloud,
Amazon Simple Queue Service (SQS), RabbitMQ
15L
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Managing the data in the Cloud: Securing data in the cloud, ACL, OAuth, OpenID,
XACML, securing data for transport in the cloud, scal ability of applications and cloud
services.
UNIT 3: DevOps and Containers in Cloud
Basics of DevOps: Introduction to DevOps, Continuous Deployment: Containerization with
Docker, Orchestration (Kubernetes and Terraform), Automating Infrastructure on Cloud,
Application Deployment and Orchestration using ECS, ECR & EKS, Application
Deployment using Beanstal k, Configuration Management using OpsWorks
Application: Designing a RESTful Web API, PubNub API for IoT to cloud, mobile device
as IoT, Mobile cloud access
15L
UNIT 4: Azure & GCP Essentials (Skill Enhancement)
Azure essentials: Azure Compute and Storage, Azure Database and Networking,
Monitoring and Managing Azure Solutions, GCP Compute and Storage, GCP Networking
and Security, Google App Engine (PaaS)
Cloud applications : Amazon Simple Notification Service (Amazon SNS), multi -player
online game hosting on cloud resources, building content delivery networks using clouds
15L
TEXTBOOKS:
1. Kevin L. Jackson. Scott Goessling, Architecting Cloud Computing Solutions,Packt
Publishing 2018
2. Shailendra Singh, Cloud Computing: Focuses on the Latest Developments in Cloud
Computing, Oxford University Press; First edition, June 2018
REFERENCE BOOKS:
1. JJ GEEWAX, Google Cloud Platform in Action, Manning Publications Co, 2018
2. Haishi Bai, Dan Stolts, Santiago Fernández Muñoz, Exam Ref 70 -535 Architecting
Microsoft Azure Solutions, Pearson Education, 2018
3. Dr. Kumar Saurabh, Cloud Computing, 4ed: Architecting Next -Gen Transformation
Paradigms, Wiley, 2017
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Course Code Course Title Credits
PSCSP3012 Practical Course on Elective -2: Advanced Computing (Trends in
Cloud Computing) 02
Note: Learners are expected to create free accounts with various Cloud Computing providers
and try to explore different technologies.
1 Using the software like / API / Tools JDK 1.7/1.8, Eclipse IDE, Dropbox API, Apache
tomcat server 7.0/8.0,Google AppEngine API, Servlets, Struts, Spring framework design
and develop Web applications using MVC Framework
2 Installing and configuring the required platform for Google App Engine
3 Studying the features of the GAE PaaS model.
4 Creating and running Web applications (Guest book, MVC) on localhost
and deploying the same in Google App Engine
5 Developing an ASP.NET based web application on the Azure platform
6 Creating an application in Dropbox to store data securely. Develop a source code using
Dropbox API for updating and retrieving files.
7 Installing Cloud Foundry in localhost and exploring CF commands.
8 Cloud application development using IBM Bluemix Cloud.
9 Installing and Configuring Dockers in localhost and running multiple images on a Docker
Platform.
10 Configuring and deploying VMs/Dockers using Chef/Puppet Automation tool
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Course Code Course Title Credits
PSCS3021 Elective -1: Security (Cryptography and Cryptanalysis) 04
Course Outcome: -
● To develop the foundation for the study of cryptography and its use in security.
● To understand the application of Number Theory and Algebra for the design of cryptographic
algorithms
● To understand the role of cryptography in communication over an insecure channel.
● To analyze and compare symmetric -key encryption and public -key encryption schemes
based on different security models
Course Specific Outcome: -
● Insights related to cryptography and cryptanalysis.
● Analyze and use methods for cryptography.
● Implement some of the prominent techniques for public -key cryptosystems and digital
signature schemes
● Understand the notions of public -key encryption and digital signatures and sketch their
formal security definitions.
UNIT 1: Classic Cryptography Techni ques
Cryptosystems and Basic Cryptographic Tools: Introduction, Secret -key Cryptosystems,
Public -key Cryptosystems, Block and Stream Ciphers, Hybrid Cryptography, Hybrid
Cryptography, Message Integrity, Message Authentication Codes, Signature Schemes,
Nonrepudiation, Certificates, Hash Functions, Cryptographic Protocols, Security
Classical Cryptography: Introduction to Some Simple Cryptosystems, Shift Cipher,
Substitution Cipher, Affine Cipher, Vigenere Cipher, Hill Cipher, Permutation Cipher,
Stream Ci phers, Cryptanalysis, Cryptanalysis of the Affine Cipher, Cryptanalysis of the
Substitution Cipher, Cryptanalysis of the Vigenere Cipher, Cryptanalysis of the Hill Cipher,
Cryptanalysis of the LFSR Stream Cipher.
Perfect Secrecy: Introduction, Perfect Secr ecy, Entropy, Properties of Entropy, Spurious
Keys, and Unicity Distance
15L
UNIT 2: Advanced Encryption, Integrity, and Authentication
Block Ciphers and Stream Ciphers: Substitution -Permutation Networks, Linear
Cryptanalysis, Differential Cryptanalysis, Data Encryption Standard, Advanced Encryption
Standard, Modes of Operation, Stream Ciphers
Hash Functions and Message Authentication: Hash Functions and Data Integrity, S ecurity
of Hash Functions, Iterated Hash Functions, Sponge Construction, Message Authentication
Codes, Unconditionally Secure MACs
15L
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RSA Cryptosystem and Factoring: Public -key Cryptography, Number Theory -Euclidean
Algorithm, Chinese Remainder Theorem, Other Useful Facts, RSA Cryptosystem, Primality
Testing, Square Roots Modulo n, Factoring Algorithms, Rabin Cryptosystem, Semantic
Security of RSA
UNIT 3: Public -Key Cryptography and Identity Verification
Public -Key Cryptography and Discrete Logarithms: Introduction, ElGamal
Cryptosystem, Shanks' Algorithm, Pollard Rho Discrete Logarithm Algorithm, Finite Fields,
Elliptic Curves, Discrete Logarithm Algorithms in Practice, Security of ElGamal Systems
Signature Schemes: Introduction to RSA Signature Scheme , Security Requirements,
ElGamal Signature Scheme, Variants of the ElGamal Signature Scheme, Full Domain Hash,
Certificates, Signing and Encrypting
Identification Schemes and Entity Authentication: Passwords and Secure Identification
Schemes, Challenge -and-Response in the Secret -key Setting, Challenge -and-Response in the
Public -key Setting, Schnorr Identification Scheme, Feige -Fiat-Shamir Identification Scheme
15L
UNIT 4: Key Management (Skill E nhancement)
Key Distribution: Attack Models and Adversarial Goals, Diffie -Hellman Key
Predistribution, Blom Scheme, Key Predistribution in Sensor Networks, Session Key
Distribution Schemes -Needham -Schroeder Scheme, Kerberos, Threshold Schemes -Shamir
Schem e
Key Agreement Schemes: Transport Layer Security (TLS), , Diffie -Hellman Key
Agreement, Known Session Key Attacks, Key Derivation Functions, MTI Key Agreement
Schemes, Deniable Key Agreement Schemes, Conference Key Agreement Schemes
Other Security Issues: Cocks Identity -based Cryptosystem, Copyright Protection,
Fingerprinting, Identifiable Parent Property, 2 -IPP Codes, Tracing Illegally Redistributed
Keys
15L
TEXTBOOKS:
1. Cryptography Theory and Practice Douglas R. Stinson, , Fourth Edition, CRC Press, 2019
2. Applied Cryptanalysis, Breaking Ciphers in Real World, John Wiley, 2015
REFERENCE BOOKS:
1. Implementing Cryptography, Shannon W. Bray, John Wiley, 2020
2. Algorithmic Cryptanalysis, Antoine Joux, CRC Press, 2017
3. Modern Cryptography: Applied Mathematics for Encryption and Information Security,
William Easttom, Springer, 2021
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Course Code Course Title Credits
PSCSP3021 Practical on Elective -1: Security (Cryptography and Cryptanalysis) 02
Note: The practical can be performed in C/C++/Java/Python
1 Program to implement password salting and hashing to create secure passwords.
2 Program to implement various classical ciphers -Substitution Cipher, Vigenère Cipher, and
Affine cipher
3 Program to demonstrate cryptanalysis (e.g., breaking Caesar or Vigener Cipher)
4 Program to implement AES algorithm for file encryption and decryption
5 Program to implement various block cipher modes
6 Program to implement Steganography for hiding mess ages inside the image file.
7 Program to implement HMAC for signing messages.
8 Program to implement Sending Secure Messages Over IP Networks
9 Program to implement RSA encryption/decryption
10 Program to implement (i) El -Gamal Cryptosystem (ii) Elliptic Curve Cryptography
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Course Code Course Title Credits
PSCS3022 Elective -2: Security (Cyber Security and Risk Assessment) 04
Course Outcome: -
● Learn about an advanced concept related to penetration testing
● Use of Kali Linux in performing penetration tests against networks, systems, and
applications
● Understand ways to protect system and digital assets
● selecting the most effective tools, to rapidly compromising network security to highlighting
the techniques used to avoid detectio n
Course Specific Outcome: -
● Develop skills to use kali Linux for penetration testing
● Use open -source tools for Reconnaissance
● Perform vulnerability assessment using popular tools
● Learn about advanced ways to exploit web apps and cloud security
● Apply techniques for privilege escalation and use exploitation tools.
UNIT 1: Introduction to Penetration Testing and Reconnaissance
Goal -based penetration testing: Introduction to Penetration Testing, Different types of
threat actors, Conceptual overview of security testing, Common pitfalls of vulnerability
assessments, penetration testing, and red team exercises, Objective -based penetration testing,
The testing met hodology Kali Linux & Red Team Tactics, Using CloudGoat and Faraday
Open -source Intelligence and Reconnaissance: Basic Principles of Reconnaissance,
Scraping, Google Hacking Database, creating custom wordlist for cracking password
Active Reconnaissance of External and Internal Networks: Stealth scanning techniques,
DNS reconnaissance, and route mapping, Employing comprehensive reconnaissance
applications, Identifying the external network infrastructure, Mapping beyond the firewall,
IDS/IPS identification, E numerating hosts, port, operating system, and service discovery,
Writing your port scanner using netcat, Large -scale scanning, Machine Learning for
Reconnaissance 15L
UNIT 2: Vulnerabilities and Advanced Attacks
Vulnerability Assessment: Local and online vulnerability databases, Vulnerability
8scanning with Nmap, Web application vulnerability scanners, Vulnerability scanners for
mobile applications, OpenVAS network vulnerability scanner, Commercial vulnerability
scanners, Specialized scanners, Threat model ing
15L
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Advanced Social Engineering and Physical Security: Common Methodology, Physical
attacks at a console, creating rough physical devices, Social Engineering Toolkit, Hiding
executables and obfuscating the attacker’s URL, Escalating an attack using DNS red irection,
Launching Phishing attack
Wireless and Bluetooth Attacks: Wireless reconnaissance, Bypassing open SSID and MAC
address authentication, attacking WPA and WPA2, Dos attacks against Wireless
communication, Compromising enterprise implementations of WPA2, Evil Twin attack,
using bettercap, WPA3, Bluetooth attacks
UNIT 3: Web and Cloud Exploitations
Exploiting Web -based applications: Web app Hacking methodology, Reconnaissance of
web apps, client -side proxies, application -specific attacks, Brows er exploitation Framework
Cloud Security Exploitation: Vulnerability scanning and application exploitation, Testing
S3 bucket misconfiguration, exploiting security permission flaws, obfuscating Cloudtail logs
Bypassing Security Controls: Bypassing Network Access Control and application -level
controls, Bypassing antivirus, Bypassing Windows OS controls
15L
UNIT 4: Exploiting System Vulnerabilities (Skill Enhancement)
Metasploit Exploitation: Metasploit framework, exploiting single and multiple targets
using MSF, using the public exploit, developing windows exploit
Privilege Escalation: Escalation methodology, escalating from domain user to system
administrator, local system escalation, escalating from administrator to system, credential
harvesting, and escalating attacks, escalating access right in active directory
Embedded devices and RFID Hacking: Firmware unpacking and updating, Introduction
to RouterSploit Framework, UART, Cloning RFID using ChameleonMini
15L
TEXTBOOKS:
1. Mastering Kali Linux for Advanced Penetration Testing Fourth Edition, Vijay Kumar Velu,
Packt, 2022
2. Learn Kali Linux 2019: Perform Powerful Penetration Testing Using Kali Linux, Metasploit,
Nessus, Nmap, And Wireshark, Glen D. Singh, Packt, 2019
REFERENCE BOOKS:
1. Hands -on Penetration Testing for Web Applications: Run Web Security Testing on Modern
Applications Using Nmap, Burp Suite and Wireshark , Richa Gupta, BPB, 2021
2. Advanced Penetration Testing, Wil Allsopp, Wiley, 2017
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Course Code Course Title Credits
PSCSP3022 Practical Course on Elective -2: Security (Cyber Security and
Risk Assessment) 02
Note: The Practical to be performed preferably on Kali Linux
1 Exploring and building a verification lab for penetration testing (Kali Linux)
2 Use of open -source intelligence and passive reconnaissance
3 Practical on enumerating host, port, and service scanning
4 Practical on vulnerability scanning and assessment
5 Practical on use of Social Engineering Toolkit
6 Practical on Wireless and Bluetooth attacks
7 Practical on Exploiting Web -based applications
8 Practical on using Metasploit Framework for exploitation.
9 Practical on injecting Code in Data Driven Applications: SQL Injection
10 Wireless Network threats (sniff wifi hotspots, analyze strength, discover wireless access
points)
Course Code Course Title Credits
PSCS3031 Elective -1: Computer Networking (Server and Data -Centric
Networking) 04
Course Outcome:
● Identify important requirements to design and support a data center.
● Determine a data center environment’s requirements including systems and network
architecture as well as services.
● Evaluate options for server farms, network designs, high availability, load balancing, data
center services, and trends that might affect da ta center designs.
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● Design a data center infrastructure integrating features that address security, performance,
and availability.
Course Specific Outcome:
● Learners will be able to know basic concepts of Server and Data -Centric Networking
● Learners will be able to know about the infrastructure of Data Centers.
● Learners will be able to know about the security measures of Data Centers.
● Learners will be able to know about network designing and virtualization.
UNIT 1: Virtualization History and Definitions
Data Center: Essential Definition, Data Center Evolution, thernet Protocol, The Humble
Beginnings of Network Virtualization,Resource Sharing Control and Management Plane,
Concepts from the Routing World, Overlapping Addresses in a Data Center
Virtual Routing and Forwarding: Defining and Configuring VRFs, VRFs and Routing
Protocols, VRFs and the Management Plane VRF -Awareness, VRF Resource Allocation
Control, Use Case: Data Center Network Segmentation
15L
UNIT 2: ACE Virtual Contexts
Application Networking Services: The Use of Load Balancers, Load -Balancing Concepts,
Security Policies, Suboptimal Traffic, Application Environment Independency, ACE Virtual
Contexts, Application Control Engine Physical Connections, Connecting an ACE Appl iance,
Bridged Design, One -Armed Design,
Managing and ConfiguringACE: Virtual Contexts, Allowing Management Traffic to a
Virtual Context, Allowing Load Balancing Traffic Through a Virtual Context, Controlling
Management Access to Virtual Contexts, ACE Vi rtual Context Additional Characteristics,
Sharing VLANs Among Contexts, Virtual Context Fault Tolerance, Instant Switches, MPLS
Basic Concepts 15L
UNIT 3: Virtualization in Server Technologies
Server virtualization: Operational Policies, Configuration, External IPMI Management
Configuration, Management IP Address, The Virtual Data Center and Cloud Computing, The
Virtual Data Center
Automation and Standardization: Cloud Implementation Example, Journey to the Cloud,
Netw orking in the Clouds, Software -Defined Networks, OpenStack, Network Overlays 15L
UNIT 4: Intelligent Disk Subsystems (Skill Enhancement) 15L
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Disk Subsystem: The architecture of Intelligent Disk Subsystems Hard Disks and Internal
I/O Channels JBOD: The Physical I/O Path from the CPU to the Storage System, SCSI, Fibre
Channel SAN, SCSI via InfiniBand and RDMA,Fibre Channel over Ethernet (FCoE) I/O
Consolidation based on Ethernet,FCoE Details,Data Center Bridging (DCB), File Systems
and Network Attached Storage (NAS)
File system Management: Local File Systems File systems and databases Journaling;,
Snapshots Volume manager Network File Systems and File Servers, N etwork Attached
Storage (NAS)
TEXTBOOKS:
1. Data Center Virtualization Fundamentals by Gustavo Alessandro Andrade Santana,Cisco
Press, 2018
2. Storage Networks Explained Wiley Publishing, 2019
REFERENCE BOOKS:
1. Information Storage and Management Wiley Publishing, 2016
2. Storage Networks: The Complete Reference, 2019
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Course Code Course Title Credits
PSCSP3031 Practical Course on Elective -1: Computer Networking (Server and
Data -Centric Networking) 02
Note: Practical can be implemented using GNS3, CISCO packet tracer 7.0 and above
01 Installation of
● Vmware Esxi
● Citrix Xen
● Microsoft Hyper -V
02 Create and manage the inter connectivity of Virtual Machine on
● Vmware Esxi
● Citrix Xen
● Microsoft Hyper -V
03 Configuring Trunks between switches and VTP Pruning
Suggested Topology
04 Configuring EtherChannels
Suggested Topology
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05 ● Configure Secure DMVPN Tunnels
● Implement a DMVPN Phase 1 Hub -to-Spoke Topology
● Implement a DMVPN Phase 3 Spoke -to-Spoke Topology
06 ● Implement BGP Path Manipulation
● Implement BGP Communities
07 ● Control Routing Updates
● Path Control Using PBR
08 ● Implement Route Redistribution Between Multiple Protocols
● Configure Route Redistribution Within the Same Interior Gateway Protocol
09 ● Implement GLBP
● Implement VRRP
● Implement HSRP
10 Implement MPLS
Course Code Course Title Credits
PSCS3032 Elective -2: Computer Networking (Wireless Networking) 04
Course Outcome: -
● To understand basic concepts of wireless networking.
● To understand 4G, 5G Technologies and their working.
● To implement Wireless architecture practically.
● To gain knowledge about sensors and their working.
Course Specific Outcome: -
● Learners will be able to know advanced concepts of wireless technologies and recent trends
in them.
● Learners will be able to implement wireless architecture practically.
● Learners will achieve the basic knowledge required as per industry standards.
● Learners will be able to know about wireless optical communication.
UNIT 1: Basic Principles of Wireless Networking
Introduction to Wireless Sensor Networks: Terminologies, Advantages, Challenges and
Applications, Types of wireless sensor networks. 15L
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Wireless Communication Technologies: Mobile Ad -hoc Networks (MANETs) and Wireless
Sensor Networks, Enabling technologies for Wireless Sensor Network s.
UNIT 2: Wireless Optical Communication(WOC)
Optical Communication: Introduction to wireless optical communication (WOC), wireless
optical channels, atmospheric channel, underwater optical channel, atmospheric losses
WOC and Applications: Weather condition influence, atmospheric turbulence effects i.e.
scintillation, beam spreading, etc. wireless optical communication application areas, WOC
challenges and applications 15L
UNIT 3: Fourth Generation Systems and New Wireless Technologies
4G Vision: 4G Features and Challenges, Applications of 4G; 4G Technologies - LTE FDD
vs TDD comparison; frame structure and its characteristics; Smart Antenna Techniques,
OFDM
Trends in Wireless Technology: MIMO Systems, Adaptive Modulation and Coding with
Time -Slot Scheduler - Bell Labs Layered Space Time (BLAST) System , Software -Defined
Radio, Cognitive Radio 15L
UNIT 4: Recent Trends in Wireless Networking (Skill Enhancement)
5G Technology: Understand 5GPP & NGMN, 5G architecture and design objective, 5G
spectrum requirements, ITU -R IMT -2020 vision for 5G, 5G RAN & Dynamic CRAN
Architecture and applications: 5G Mobile Edge Computing & Fog computing, 5G Protocol
Stack, 5G Ultra -dense networks, 5G Air interface, Applications 15L
TEXTBOOK:
1. Anurag Kumar, D.Manjunath, Joy kuri, ―Wireless Networking‖, third Edition, Elsevier
2018.
REFERENCE BOOKS:
1. Jochen Schiller, ‖Mobile Communications‖, Second Edition, Pearson Education 2019.
2. Vijay Garg, ―Wireless Communications and networking‖, First Edition, Elsevier 2012.
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Course Code Course Title Credits
PSCSP3032 Practical Course on Elective -2: Computer Networking (Wireless
Networking) 02
Note: Practical can be implemented using GNS3, CISCO packet tracer 7.0 and above
1 Configuring WEP on a Wireless Router
2 Demonstrating Distribution Layer Functions
3 Placing ACLs
4 Planning Network -based Firewalls
5 Configure Auto Profiles ACU Utilities
6 Creating an Adhoc Network
7 Configuring Basic AP Settings
8 Configure Ethernet/Fast Ethernet Interface
9 Configure Radio Interfaces through the GUI
10 Configure Site -to-Site Wireless Link
Course Code Course Title Credits
PSCS3041 Elective -1: Data Science (Data Visualization) 04
Course Outcome: -
● Familiarity with working with data analysis tools.
● Ability to perform data wrangling for practical purposes.
● Ability to solve real -world data analysis problems with thorough, detailed examples.
● Ability to use Tableau to handle data from various sources and perform analysis of data.
Course Specific Outcome: -
● Understands the fundamentals of Visualization.
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● Working with different Data Collection Structures.
● Efficiently handle various source data using Tableau.
● Data Visualization and Analysis can be performed using Tableau.
● Handling and customizing Geospatial data using Tableau.
● Creating a story using the dashboard to analyze data
UNIT 1: Preparing and Storing Data
Series : Creating a Series with index, creating a Series from a Dictionary, Creating a Series
from a Scalar Value, Vectorized Operations and Label Alignment with Series, Name
Attribute. Accessing Data from a Series with a Position, Exploring and Analysing a Series,
Operations on a Series.
Data Frames : Creating a Data Frame from a Dict of Series or Dicts, Creating Data Frames
from a Dict of Ndarrays, Creating Data Frames from a Structured or Record Array, Creating
Data Frames from a List of Dicts, Creating Data Frames from a Dict of Tuples, Selecting,
Adding, and Deleting Data Frame Columns, Assigning New Columns in Method Chains,
Row Selection, Row Ad dition, Row Deletion, Exploring and Analysing a Data Frame,
Indexing and Selecting Data Frames, Transposing a Data Frame, Data Frame Interoperability
with Numpy Functions.
Visualizing Data: Data visualization in Business Intelligence, Data visualization t echniques.
Data visualization libraries in Python
15L
UNIT 2: Data Cleaning and Data Wrangling
Data Gathering and Cleaning: Cleaning Data, Checking for Missing Values, Handling the
Missing Values, Reading and Cleaning CSV Data, Merging and Integrating Data, Reading
Data from the JSON Format, HTML Format, XML Format.
Data Transformation Removing Duplicates: Replacing Values, Renaming Axis Indexes
Hierarchical Indexing: Reordering and Sorting Levels Summary' Statistics by Level
Indexing with a DataFrame’ s columns. Combining and Merging Datasets Database -Style,
DataFrame Joins Merging on Index Concatenating Along with an Axis Combining Data with
Overlap
Reshaping and Pivoting: Reshaping with Hierarchical Indexing Pivoting “Long” to “Wide”
Format Pivoting “ Wide” to “Long” Format
Statistical Analysis, Data Aggregation: Data Grouping, Iterating Through Groups,
Aggregations, Transformations, Filtration. 15L
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UNIT 3: Basics of Tableau
Tableau: Introduction, connecting to data, Visualizing Data using Tableau, Gra phs, charts,
and reports
Connecting to Data: Connecting various data sources, Managing data source metadata,
Extract Data, Filtering data. Moving beyond basic visualization.
Calculations: Introduction to Calculation, Row -level Calculations, Aggregate calcu lations,
parameters, Leveraging level of Detail Calculations.
Telling Data Story with Dashboards: Designing Dashboards in tableau, Designing for
different displays and devices. 15L
UNIT 4 : Data Visualization (Skill Enhancement)
Trend Visualization: Trend Models, Analysing Trend Models. Clustering, Distributions,
and Forecasting, Different Charts and Visualization.
Dynamic Dashboards: Sheet Swapping, Automatically Showing and hiding controls.
Exploring Mapping and Advanced Geospatial Features: Rendering m aps with Tableau.
Using Geospatial Data Creating custom territories.
Structuring Messy Data to Work Well in Tableau: Structuring data for Tableau.
Taming data with Tableau Prep: Cleaning, Transforming, Filtering, and Calculating data.
Sharing Data story. 15L
TEXTBOOKS:
1. Dr. Ossama Embarak, Data Analysis and Visualization Using Python, Apress, 2018
2. Wes McKinney, “Python for Data Analysis: Data Wrangling with Pandas, NumPy, and
IPython”, O’Reilly, 2nd Edition, 2018.
3. Learning Tableau 2020, Create effective d ata visualizations, build interactive visual
analytics, and transform your organization. Joshua Milligan, Fourth Edition, Packt, 2020.
REFERENCE BOOKS:
1. Jake VanderPlas, “Python Data Science Handbook: Essential Tools for Working with Data”,
O’Reilly, 2017
2. Visual Data Storytelling with Tableau, Linda Ryan, Pearson Addison Wesley Data &
Analytics Series, 2018
3. Visual Analytics with Tableau, Alexander Loth, Wiley, 2019
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Course Code Course Title Credits
PSCSP3041 Practical Course on Elective -1: Data Science (Data Visualization) 02
Note: Practical can be implemented using Python / R studio.
1 Create one -dimensional data using series and perform various operations on it.
2 Create Two -dimensional data with the help of data frames and perform different operations
on it.
3 Write a code to read data from the different file formats like JSON, HTML, XML, and CSV
files and check for missing data and outlier values and handle them.
4 Perform Reshaping of the hierarchical data and pivoting data frame data.
5 Connecting and extracting with various data resources in tableau.
6 Performing calculations and creating parameters in Tableau.
7 Designing Tableau Dashboards for different displays and devices.
8 Create a Trend model using data, Analyse -it and use it for forecasting.
9 Creating Geospatial feature maps in Tableau using Geospatial Data.
10 Create Dashboard and Storytelling using tableau.
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Course Code Course Title Credits
PSCS3042 Elective -2: Data Science (Big Data Analytics) 04
Course Outcome: -
● Exposure to the fundamentals of business intelligence and big data analytics.
● Understand basic concepts in Big Data analytics and parallel data processing.
● Understand Hadoop Technology and its applications.
● Exposure to real -life applications and solving them using big data toolkits.
Course Specific Outcome: -
● Understands big data and the technologies associated with it.
● Identify Big Data and its Business Implications.
● List and understands the components of Hadoop and the Hadoop Ecosystem.
● Understands Ma p-Reduce Technology and its applications.
● Understands handling of data using Spark Technology.
● Understands the Hive, Sqoop, and Pig Technology.
UNIT 1: Big Data and Hadoop
Big Data: Characteristics of Big Data, Big Data importance, and Applications, Big Data
Analytics, Typical Analytical Architecture, Requirement for new analytical architecture,
Challenges in Big DataAnalytics, Need of big data frameworks, Types and Sources of Big
Data.Exploring the Use of Big Data in Business Context
Hadoop Framework: Requirement of Hadoop Framework, Design principle of Hadoop,
Hadoop Components, Hadoop Ecosystem, Hadoop 2 architecture, Hadoop YARN
Architecture, Advantage of YARN, YARN Command. HDFS: Design of HDFS, Benefits
and Challenges, HDFS Commands.
15L
UNIT 2: Map Reduce and HBASE
MapReduce Framework and Basics: Working of Map Reduce, Developing Map Reduce
Application, I/O formats, Map side join, Reduce Side Join, Secondary sorting, Pipelining
MapReduce jobs. Processing data using Map Reduce.
HBASE: Role of HBase in Big Data Processing, Features of HBase. HBase Architecture,
Zookeeper. HBase Commands for creating, listing, and Enabling data tables.
15L
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UNIT 3: Spark Framework and A pplications
Introduction to Spark: Overview of Spark, Hadoop vs Spark, Cluster Design, Cluster
Management, performance, Application Programming Interface (API): Spark Context,
Resilient Distributed Datasets, Creating RDD, RDD Operations, Saving RDD - Lazy
Operation, Spark Jobs.
Writing Spark Application – Compiling and Running the Application. Monitoring and
debugging Applications. Spark Programming.
15L
UNIT 4 : Tools for Data Anlytics (Skill Enhancement)
Spark SQL: SQL Context, Importing and Saving data, Data frames, using SQL, GraphX
overview, Creating Graph, Graph Algorithms.
Spark Streaming: Overview, Errors and Recovery, Streaming Source, Streaming live data
with spark
Hive: Hive services, Data Types, and Built -in functions in Hive.
Pig: Working with operators in Pig, Working with Functions and Error Handling in Pig
Flume and Sqoop: Flume Architecture, Sqoop, Importing Data. Sqoop2 vs Sqoop.
15L
TEXTBOOKS:
1. Big Data Analytics, Introduction to Hadoop, Spark, and Machine -Learning, Raj Kamal,
Preeti Saxena, McGraw Hill, 2019
2. Big Data, Black Book: Covers Hadoop 2, MapReduce, Hive, YARN, Pig, R and Data
Visualization, Dreamtech Press; 1st edition, 2016
3. Big Data Analytics with Spark, A Practitioner's Guide to Using Spark for Large Scale Data
Analysis, Apress, 2015
4. Hadoop MapReduce v2 Cookbook - Second Edition, Packt Publishing, 2015
REFERENCE BOOKS:
1. Big Data in Practice: How 45 Successful Companies U sed Big Data Analytics to Deliver
Extraordinary Results, Wiley, 1st edition, 2016
2. Hadoop – TheDefinitive Guide by Tom White, OReilly, 2012
3. Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data,
McGrawHill, 2012
4. Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's
Businesses, Michael Minelli, Wiley, 2013
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Course Code Course Title Credits
PSCSP3042 Practical Course on Elective -2: Data Science (Big Data Analytics) 02
Note: Following practical can be performed on Windows or Linux
1 Installing and setting environment variables for Working with Apache Hadoop.
2 Implementing Map -Reduce Program for Word Count problem,
3 Download and install Spark. Create Graphical data and access the graphical data using
Spark.
4 Write a Spark code for the given application and handle error and recovery of data.
5 Write a Spark code to Handle the Streaming of data.
6 Install Hive and use Hive Create and store structured databases.
7 Install HBase and use the HBase Data model Store and retrieve data.
8 Perform importing and exporting of data between SQL and Hadoop using Sqoop.
9 Write a Pig Script for solving counting problems.
10 Use Flume and transport the data from the various sources to a cen tralized data store.
SEMESTER – IV
Course Code Course Title Credits
PSCS401 Robotics (Online Mode) 04
Course Outcome: -
● Leverage the features of the Raspberry Pi OS
● Discover how to configure a Raspberry Pi to build an AI -enabled robot
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● Interface motors and sensors with a Raspberry Pi
● Code robot to develop engaging and intelligent robot behavior
● Explore AI behavior such as speech recognition and visual processing
Course Specific Outcome: -
● Knowledge about the fundamentals of Robotics and its applic ations
● Ability to use Raspberry Pi for programming Robotics
● Ability to apply robotics in speech and vision problems
UNIT 1: Introduction to Robotics
Introduction to Robotics: What is a robot? Examples of Advanced and impressive robots,
Robots in the home, Robots in industry
Robotics in Action: Exploring Robot Building Blocks - Code and Electronics Technical
requirements, Introducing the Raspberry Pi - Starting with Raspbian Technical requirements,
Raspberry Pi controller on a robot
15L
UNIT 2: Building Robot Basics
Technical requirements : Robot chassis kit with wheels and motors, a motor controller,
Powering the robot, Test fitting the robot, Assembling the base.
Robot Programming: Programming technique, adding line sensors to our robot, creatin g
line-sensing behavior, and Programming RGB Strips in robot.
15L
UNIT 3: Servo Motors
Use and control of servo motors, pan, and tilt mechanism.Distance sensors, Introduction to
distance sensors and their usage
Connecting distance sensors to robot and their testing. Creating a smart object avoidance
behavior. Creating a menu to select different robot behaviors, Distance and speed measuring
sensors —encoders and odometry
15L
UNIT 4: Robot Vision and Voice Commun ication (Skill Enhancement)
Robotics setup: Setting up a Raspberry Pi Camera on the robot (software and hardware),
Check the robot vision on a phone or laptop, Mask images with RGB strips
15L
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Robotics for Vision and Voice Applications: Colors, masking, and fil tering – chasing
colored objects, Detecting faces with Haar cascades, Finding objects in an image, Voice
Communication with a robot
TEXTBOOKS:
1. Danny Staple, Robotics Programming, Packt Publishing, 2nd edition, Feb 2021
REFERENCE BOOKS:
1. Saeed B. Niku, Introduction to Robotics: Analysis, Control, Applications, Wiley, 3rd
Edition, 2019
2. D. K. Pratihar, FUNDAMENTALS OF ROBOTICS. Narosa Publication, 2016
3. Lentin Joseph, Learning Robotics Using Python, Packt Publishing Ltd., May 2015
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Course Code Course Title Credits
PSCSP401 Practical Course on Robotics (Online Mode) 02
Note: Following practical can be performed using Python and simulators, Raspberry Pi, and
other hardware devices.
1 Making a Raspberry Pi headless, and reaching it from the network using WiFi and SSH.
2 Using sftp upload files from PC.
3 Write Python code to test motors.
4 Write a script to follow a predetermined path.
5 Develop Python code for testing the sensors.
6 Add the sensors to the Robot object and develop the line -following behavior code.
7 Using the light strip develop and debug the line follower robot.
8 Add pan and tilt service to the robot object and test it.
9 Create an obstacle avoidance behavior for robot and test it.
10 Detect faces with Haar cascades.
11 Use the robot to display its camera as a web app on a phone or desktop, and then use the
camera to drive smart color and face -tracking behaviors.
12 Use a Raspberry Pi to run the Mycroft environment and connect it to a
speaker/microphone combination
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Course Code Course Title Credits
PSCS402 Advanced Deep Learning (Online Mode) 04
Course Outcome: -
● Understand the context and use of neural networks and deep learning
● Understand the tools and libraries for deep learning
● Have a working knowledge of neural networks and deep learning
● Explore the parameters for neural networks
● Identify emerging applications of deep learning
Course Specific Outcome: -
● Knowledge of implementing neural network architectures for deep learning.
● Skill to implement regularization and optimization of neural network
● Ability to implement advanced networks like CNN, RNN and GAN
● Implement deep learning for advanced applications like object identification, speech, and
languag e
UNIT 1: Neural Network for Deep Learning
Optimization and Neural Network: Review of Neural Network fundamentals, the
problem of Learning, Implementing single Neuron -Linear and Logistic Regression,
Deep Learning: Fundamentals, Deep Learning Appli cations, Popular open -source
libraries for deep learning
Feed -Forward Networks : Networks architecture and Matrix notation, Overfitting,
Multiclass Classification with Feed -Forward Neural Networks, Estimating Memory
requirement of Models 15L
UNIT 2: Convolutional and Recurrent Networks for Deep Learning
Regularization: Complex Network and Overfitting, Regularization and related concepts,
Hyperparameter tuning
Convolutional Neural Networks: Kernels and Filters,Building Blocks of CNN, Inception
Network, Transfer Learning
Recurrent Neural Network: Notation and Idea of recurrent neural networks, RNN
Topologies, backpropagation through time, vanishing and exploding gradients 15L
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UNIT 3: Advanced Concepts for Deep Learning
Autoencoders : Introduction, Network Design, Regularization in Autoencoders, Denoising
autoencoders, Feed -Forward Autoencoders, spare and Contractive autoencoders
Unsupervised Feature Learning: Hopfield networks and Boltzmann machines, restricted
Boltzmann machine, Deep belief networks
Gener ative Adversarial Networks (GANs): Introduction, training algorithms,
Conditional GANs, applications, Deep convolutional generative adversarial networks 15L
UNIT 4: Deep Learning Application(Skill enhancement)
Deep Learning for AI Games: AI Game Playing, Reinforcement learning, Maximizing
future rewards, Q -learning, The deep Q -network as a Q -function, Balancing exploration
with exploitation, Experience replay, or the value of experience
Deep Learning for Object Localization and classification: Intersect Ov er Union (IoU),
Sliding Window Approach, Region -Based CNN (R -CNN)
Deep Learning for Language Modelling and Speech Recognition 15L
TEXTBOOKS:
1. Python Deep Learning, Valentino Zocca, Packt Publication, 2017
2. Applied Deep Learning, with TensorFlow 2, Umberto Michelucci, Apress, 2022
3. Pro Deep Learning with TensorFlow, Santanu Pattanayak, Apress, 2017
REFERENCE BOOKS:
1. Advanced Deep Learning with Keras, Rowel Atienza, Packt Publication, 2018
2. Python Deep Learning Cookbook, Indra den Bakker, Packt Publication, 2017
3. Deep Learning with Keras, Antonio Gulli, Packt Publication, 2017
Course Code Course Title Credits
PSCSP402 Practical Course on Advanced Deep Learning (Online Mode) 02
Note: Following practical can be performed using python
1 Implement Feed -forward Neural Network and train the network with different optimizers
and compare the results.
2 Write a Program to implement regularization to prevent the model from overfitting
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3 Implement deep learning for recognizing classes for datasets like CIFAR -10 images for
previously unseen images and assign them to one of the 10 classes.
4 Implement deep learning for the Prediction of the autoencoder from the test data (e.g.
MNIST data set)
5 Implement Convolutional Neural Network for Digit Recognition on the MNIST Dataset
6 Write a program to implement Transfer Learning on the suitable dataset (e.g. classify the
cats versus dogs dataset from Kaggle).
7 Write a program for the Impleme ntation of a Generative Adversarial Network for
generating synthetic shapes (like digits)
8 Write a program to implement a simple form of a recurrent neural network.
a. E.g. (4 -to-1 RNN) to show that the quantity of rain on a certain day also depends
on the values of the previous day
b. LSTM for sentiment analysis on datasets like UMICH SI650 for similar.
9 Write a program for object detection from the image/video.
10 Write a program for object detection using pre -trained models to use object detection.
Course Code Course Title Credits
PSCSP403 Internship with Industry 06
Context:
An internship offers an environment for the student to apply what he or she has learned in the
classroom in a real -world setup. It also equips the student with the technical and non -technical
skills required by the industry. An organization, in turn, gets an opportunity to understand and
appreciate the curriculum of the program and will be in a position to offer constructive feedback
on the course and industry requir ements. Faculty will get first -hand exposure to understand the
industry and the type of work they do, which will help to improve the pedagogy and delivery.
Internship details:
• Internship should be of 2 to 3 months with 8 to 12 weeks duration.
• The student is expected to devote at least 300 hours physically at the organization.
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• The student is expected to find an internship by himself or herself. However, the
institution should assist their students in getting an internship in good
organizations .
• The home institution cannot be taken as the place of internship.
• Internship can be on any topic covered in the syllabus.
Interning organization: Internship can be done, in one of the following, but not restricted to,
types of organizations:
▪ Software development firms
▪ Hardware/ manufacturing firms
▪ Any small -scale industries, service providers like banks
▪ Clinics/ NGOs/professional institutions like that of CA, Advocate, etc
▪ Civic Depts like Ward office/post office/police station/ panchayat.
▪ Research Centres/ University Depts/ College as research Assistants for research
projects or similar capacities.
Internship mentors:
To ensure the rigor of the MSc program, a student will be provided with a faculty mentor
provided by the institution and an in dustry mentor, to be provided by the organization where
the student is interning with.
● The industry mentor ensures that the requirements of the organization and the demands
of the project are done by the internee.
● The faculty mentor is the overall in charge of the internship. He or she could evaluate
the quality of the internship in a uniform manner across all students and within the
demand of the program.
Documentation of the internship:
The student will make two documents as part of the internship.
● Online diary: This ensures that the student updates daily activity, which could be accessed
by both the mentors. Daily entry can be of 3 - 4 sentences giving a very brief account of the
learning/activities/interaction taken place. The faculty mentor will be monitoring the entries
in the diary regularly.
● Internship report: A student is expected to make a report based on the internship he or
she has done in an organization. It should contain the following:
▪ Certificate : A certificate in the prescribed Performa (given in appendix 1) from the
organization where the internship was done.
▪ Title : A suitable title giving the idea about what work the student has performed during
the internship.
▪ Evaluation form: The form filled by the supervisor or to whom the intern was reporting, in the
prescribed Performa (given in appendix 2).
▪ Description of the organization : A small description of the organization where the
student has interned
▪ Description of the activities done by the section where the intern has worked: A
descripti on of the section or cell of the organization where the intern worked. This
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should give an idea about the type of activity a new employee is expected to do in that
section of the organization.
▪ Description of work allotted and done by the intern: A detailed description of the work
allotted, and actual work performed by the intern during the internship period. It shall
be the condensed and structured version of the daily report mentioned in the online
diary.
▪ Self-assessment : A self -assessment by the intern on what he or she has learned during
the internship period. It shall contain both technical as well as interpersonal skills
learned in the process.
The internship report needs to be submitted to the external examiner at the time of the
University examinatio n.
Interaction between mentors:
To ensure the smooth conduct of the internship a meet -up involving the intern, industry mentor,
and the faculty mentor will be scheduled as a mid -term review. The meeting can preferably be
online to save time and resources. The meeting ensures the synergy between all stakeholders
of the internship. A typical meeting can be of around 15 minutes where at the initial stage the
intern brief about the work and interaction goes for about 10 minutes. This can be followed by
the inte raction of the mentors in the absence of the intern. This ensures that issues between the
intern and the organization, if any, are resolved amicably.
Intern ship workload for the faculty:
Every student is provided with a faculty member as a mentor. So, a faculty mentor will have a
few students under him/her. A faculty mentor is the overall in charge of the internship of the
student. He/she constantly monitors the progress of the internship by regularly overseeing the
diary, interacting with the industry me ntor, and guiding on the report writing etc. Considering
the time and effort involved, a faculty mentor who is in -charge of 20 students shall be provided
by a workload of 3 hours.
Course Code Course Title Credits
PSCSP404 Project Implementation 06
Guidelines for Project Implementation in Semester - IV
● A student is expected to devote at least 3 to 4 months of effort to the implementation.
● Students should submit a detailed project implementation report at the time of viva.
Guidelines for Documentation of Project Proposal in Semester –IV
A student should submit a project implementation report with the following details:
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● Title: Title of the project.
● Objective: A detailed objective of the proposal is needed.
● Related works: A detailed survey of the relev ant works done by others in the domain.
The student is expected to refer to at least 15 recent (last five years) research papers in
addition to textbooks and web links in the relevant topic.
● Methodology: A proper and detailed procedure of how to solve the problem discussed.
It shall contain the techniques, tools, software, and data to be used.
● Implementation details: A description of how the project has been implemented.
● Experimental setup and results: A detailed explanation of how experiments were
conduct ed, what software was used, and the results obtained. Details like screenshots,
tables, and graphs can come here.
● Analysis of the results: A description of what the results mean and how they have been
arrived at. Different performing measures or statistical tools used etc may be part of
this.
● Conclusion: A conclusion of the project performed in terms of its outcome
● Future enhancement: A sm all description of what enhancement can be done when
more time and resources are available
● Program code: The program code may be given as an appendix.
The project documentation needs to be signed by the teacher in charge and head of the
Department. Studen t should also attach the certified copy of the internal evaluation report
(Appendix III) at the time of Project evaluation and viva as part of the University examination.
4. EVALUATION
The evaluation of each paper shall contain two parts:
Internal Assessment - 40 Marks.
External Assessment - 60 Marks.
The Internal to External assessment ratio shall be 2:3.
5. SCHEME OF EXAMINATIONS AND DISTRIBUTION PATTERN
OF MARKS
Theory Examination (Semester -III)
a. Scheme of Internal Evaluation (40 Marks)
Assessment consists of two class tests of 20 marks each. The first -class test is to be
conducted when approx. 30% syllabus is completed and the second class test when an
additional 70% syllabus is completed. The duration of each test shall be one hour. The test
should be conducted in online mode on the LMS system in the institute’s premises.
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b. Scheme of External Examination (60 Marks)
The External Theory examination of all semesters shall be conducted by the University at
the end of each semester.
Q.1 Attempt any two of the following (12)
a) 6
b) 6
c) 6
d) 6
Q.2 Attempt any two of the following (12)
a) 6
b) 6
c) 6
d) 6
Q.3 Attempt any two of the following (12)
a) 6
b) 6
c) 6
d) 6
Q.4 Attempt any two of the following (12)
a) 6
b) 6
c) 6
d) 6
Q.5 Attempt any two of the following (12)
a) 6
b) 6
c) 6
d) 6
Practical Examination (Semester -III)
a. Scheme of Internal Evaluation
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There will not be any internal examination for practical courses of Semester - III.
b. Scheme of External Examination
The particulars of the external examination for each practical course of Semester - III will be of
TWO hours. The details of the same are given below:
No Semester Course Code Particular No of
Questions Marks Total
Marks
1
III PSCSP3011 /
PSCSP3012 Laboratory experiment
question with internal
choice
02 40
50
Journal 05
VIVA 05
2 PSCSP3021/
PSCSP3022 Laboratory experiment
question with internal
choice
02 40
50
Journal 05
VIVA 05
3 PSCSP3031/
PSCSP3032 Laboratory experiment
question with internal
choice
02 40
50
Journal 05
VIVA 05
4 PSCSP3041/
PSCSP3042 Laboratory experiment
question with internal
choice
02 40
50
Journal 05
VIVA 05
Theory Examination (Semester -IV)
a. Scheme of Internal Evaluation (40 Marks)
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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 the second class test when an
additional 40% syllabus is completed. The duration of each test shall be one hour. The test
should be conducted in online mode on the LMS system on the instit ute’s premises.
b. Scheme of External Examination (60 Marks)
The External Theory examination of all semesters shall be conducted by the University at
the end of each semester.
Q.1 Attempt any two of the following (12)
a) 6
b) 6
c) 6
d) 6
Q.2 Attempt any two of the following (12)
a) 6
b) 6
c) 6
d) 6
Q.3 Attempt any two of the following (12)
a) 6
b) 6
c) 6
d) 6
Q.4 Attempt any two of the following (12)
a) 6
b) 6
c) 6
d) 6
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Q.5 Attempt any two of the following (12)
a) 6
b) 6
c) 6
d) 6
Practical Examination (Semester -IV)
a. Scheme of Internal Evaluation
There will not be any internal examination for practical courses of Semester - IV.
Sr.
No. Semester Course
Code Particulars Marks Total
Marks
1. IV PSCSP403 Internship with
Industry Online diary 30 60 Mid-term interaction 30
2. IV PSCSP404 Project
Implementation Documentation 30 60 Presentation 30
b. Scheme of External Examination
The particulars of the external examination for each practical course of Semester - IV will be
of TWO hours. The details of the same are given below:
No Semester Course
Code Particular No of
Questions Marks Total
Marks
1.
IV PSCSP401 Laboratory experiment
question with internal
choice 02 40
50
Journal 05
VIVA 05
2. PSCSP402 Laboratory experiment
question with internal
choice 02 40
50
Journal 05
VIVA 05
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3. PSCSP403 Quality & Relevance 40 90 Internship VIVA 50
4. PSCSP404 Quality & Relevance 40 90 Project VIVA 50
Guidelines of Journals:
A student should maintain a Journal with Practical experiments reported for
each of the practical courses of Semester - III and Semester - IV. Related theories/algorithms
need to be explained in a journal.
Certified Journal with at least 70% of the list of the Practical need to be submitted at the time
of the practical examination.
-----------------
Appendix -I
(Proforma for the certificate for internship in official letter head)
This is to certify that Mr/Ms______________ ________________________________
of _______________________________College/Institution worked as an intern as part of
his/her M.Sc. course in Computer Science of University of Mumbai. The particulars of
internship are given below:
Internship starting date: _______________
Internship ending date: ________________
Actual number of days worked: ______________
Tentative number of hours worked: __________ Hours
Broad area of work: ______________________________________________
A small description of wo rk done by the intern during the period:
_____________________________________________________________________
_____________________________________________________________________
Signature: ____________________
Name: _______________________
Designa tion: __________________
Contact number: _______________
Email: _______________________
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(Seal of the organization)
Appendix -II
(Proforma for the Evaluation of the intern by the industry mentor /to whom the intern was
reporting in the organization)
Professional Evaluation of intern
Name of intern: _____________________________________________
College/institution: ________________________________________
[Note: Give a score in the 1 to 5 scale by putting √ in the respective cells]
No Particular Excellent Very Good Good Moderate Satisfactory
1 Attendance & Punctuality
2 Ability to work in a team
3 Written and oral
communication skills
4 Problem solving skills
5 Ability to grasp new concepts
6 Technical skill in terms of
technology, programming etc
7 Ability to complete the task
8 Quality of overall work done
Comments: _________________________________________________________________
___________________________________________________________________________
Signature: _______________________________
Name: __________________________________
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Designation: _____________________________
Contact number: __________________________
Email: __________________________________
(Seal of the organization)
Appendix -III
Maintain the weekly online diary for each week in the following format.
WEEK
No Day Date Name of the Topic/Module
Completed Remarks
MONDAY
TUESDAY
WEDNESDAY
THRUSDAY
FRIDAY
SATURDAY
Signature of the Faculty mentor: ___________
Seal of the University/College
----xxxx ----