Vide Item No 642 R Five Branches CBCS Sem V VI Rev 2019 C Scheme_1 Syllabus Mumbai University

Page 1

Page 2

Copy to : -
1. The Deputy Registrar, Academic Authorities Meetings and Services
(AAMS),
2. The Deputy Registrar, College Affiliations & Development
Department (CAD),
3. The Deputy Registrar, (Admissions, Enrolment, Eligibility and
Migration Department (AEM),
4. The Deputy Registrar, Research Administration & Promotion Cell
(RAPC),
5. The Deputy Registrar, Executive Authorities Section (EA),
6. The Deputy Registrar, PRO, Fort, (Publi cation Section),
7. The Deputy Registrar, (Special Cell),
8. The Deputy Registrar, Fort/ Vidyanagari Administration Department
(FAD) (VAD), Record Section,
9. The Director, Institute of Distance and Open Learni ng (IDOL Admin),
Vidyanagari,
They are requested to treat this as action taken report on the concerned
resolution adopted by the Academic Council referred to in the above circular
and that on separate Action Taken Report will be sent in this connection.

1. P.A to Hon’ble Vice -Chancellor,
2. P.A Pro -Vice-Chancellor,
3. P.A to Registrar,
4. All Deans of all Faculties,
5. P.A to Finance & Account Officers, (F.& A.O),
6. P.A to Director, Board of Examinations and Evaluation,
7. P.A to Director, Innovation, Incubation and Linkages,
8. P.A to Director, Board of Lifelong Learning and Extension (BLLE),
9. The Director, Dept. of Information and Communication Technology
(DICT) (CCF & UCC), Vidyanagari,
10. The Director of Board of Student Development,
11. The Director, Dep artment of Students Walfare (DSD),
12. All Deputy Registrar, Examination House,
13. The Deputy Registrars, Finance & Accounts Section,
14. The Assistant Registrar, Administrative sub -Campus Thane,
15. The Assistant Registrar, School of Engg. & Applied Sciences, Kalyan ,
16. The Assistant Registrar, Ratnagiri sub -centre, Ratnagiri,
17. The Assistant Registrar, Constituent Colleges Unit,
18. BUCTU,
19. The Receptionist,
20. The Telephone Operator,
21. The Secretary MUASA

for information.

Page 3

AC – 11 July, 2022
Item No. – 6.42




University of Mumbai








Syllabus for
B.E.(Computer Engineering )

1. Computer Science and Engineering (Data Science)

2. Computer Science and Engineering (Artificial
Intelligenceand Machine Learning)

3. Artificial Intelligence and Data Science

4. Artificial Intelligence and Machine Learning


5. Data Engineering

(V& VI)
(Choice Based Credit System)

(Introduced from the academic year 2022 -23)

Page 4

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 Technology (in particular Engineering) of University of Mumbai has taken a lead in
incorporating philosophy of outcome based education in the process of curriculumdevelopment.
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 enha nce learner‘s learning process. Choice based Credit and grading
system enables a much -required shift 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 assignment 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 syllabusetc.
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 s tudents in
terms of direct contact hours. In this regard, faculty of science and technology 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 skill 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 the 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. Subsequ ently 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 DrAnuradhaMuzumdar
AssociateDean Dean
Faculty of ScienceandTechnology Faculty of Science andTechnology
Universityof 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 r ecommendation of AICTE model curriculum overall credits
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 c ourses, 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 Enginee ring, are very happy to
present Third Year Computer Engineering 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.
Computer Engin eering is one of the most sought -after courses amongst engineering students. The syllabus
needs revision in terms of preparing the student for the professional scenario relevant and suitable to cater
the needs of industry in present day context. The syllab us 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 acquainted 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 syll abus 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 affil iating University, AICTE, UGC, and various
accreditation agencies by keeping an eye on the technological developments, 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 show case their talent by doing innovative
projects that strengthen their profile and increases the chance 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. LeenaRag ha : 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 THIRD
YEAR UNIVERSITYOFMUMBAI(With Effectfrom2022 -
2023)
SemesterV

Course
Code
CourseName Teaching
Scheme (Contact
Hours) Credits Assigned
Theory Pract. Theory Pract. Total
CSC501 ComputerNetwork 3 -- 3 -- 3
CSC502 WebComputing 3 -- 3 3
CSC503 ArtificialIntelligence 3 -- 3 -- 3
CSC504 DataWarehousing&
Mining 3 -- 3 -- 3
CSDLO5
01X DepartmentLevel
OptionalCourse -1 3 -- 3 -- 3
CSL501 WebComputingand
NetworkLab -- 2 -- 1 1
CSL502 ArtificialIntelligenceLab -- 2 -- 1 1
CSL503 DataWarehousing &
MiningLab -- 2 -- 1 1
CSL504 BusinessCommunication
andEthics -II -- 2*+2 -- 2 2
CSM501 Mini Project: 2A -- 4$ -- 2 2
Total 15 14 15 07 22



Course
Code


CourseName ExaminationScheme
Theory Term
Work Pract
&oral Total
Internal A
ssessment End
Sem
Exam Exam.
Duration
(inHrs)
Test1 Test2 Avg
CSC501 ComputerNetwork 20 20 20 80 3 - -- 100
CSC502 WebComputing 20 20 20 80 3 -- -- 100
CSC503 ArtificialIntelligence 20 20 20 80 3 -- -- 100
CSC504 DataWarehousing&
Mining 20 20 20 80 3 -- -- 100
CSDLO5
01X DepartmentLevelOptional
Course -1 20 20 20 80 3 -- -- 100
CSL501 WebComputingand
NetworkLab -- -- -- -- -- 25 25 50
CSL502 ArtificialIntelligenceLab -- -- -- -- -- 25 25 50
CSL503 DataWarehousing&
MiningLab -- -- -- -- -- 25 25 50
CSL504 BusinessCommunication
andEthics -II -- -- -- -- -- 50 -- 50
CSM501 MiniProject:2A -- -- -- -- -- 25 25 50
Total -- -- 100 400 -- 175 100 775

*Theoryclasstobeconductedforfullclassand$indicatesworkloadofLearner(NotFaculty),studentscan
form
groupswithminimum2(Two)andnotmorethan4(Four).FacultyLoad:1hourperweekper fourgroups.

Page 9

PROGRAM STRUCTURE FOR THIRD YEAR
UNIVERSITY OF MUMBAI (With Effect from 2022 -2023)
Semester VI
Course
Code Course Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Pract.
Tut. Theory Pract. Total
CSC601 Data Analytics and
Visualization 3 -- 3 -- 3
CSC602 Cryptography and System
Security 3 -- 3 3
CSC603 Software Engineering and
Project Management 3 -- 3 -- 3
CSC604 Machine Learning 3 -- 3 -- 3
CSDLO6
01X Department Level Optional
Course -2 3 -- 3 -- 3
CSL601 Data Analytics and
Visualization Lab -- 2 -- 1 1
CSL602 Cryptography & System
Security Lab -- 2 -- 1 1
CSL603 Software Engineering and
Project Management Lab -- 2 -- 1 1
CSL604 Machine Learning Lab -- 2 -- 1 1
CSL605 Skill base Lab Course:
Cloud Computing -- 4 -- 2 2
CSM601 Mini Project Lab: 2B -- 4$ -- 2 2
Total 15 16 15 08 23
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
CSC601 Data Analytics and
Visualization 20 20 20 80 3 -- -- 100
CSC602 Cryptography and System
Security 20 20 20 80 3 -- -- 100
CSC603 Software Engineering and
Project Management 20 20 20 80 3 -- -- 100
CSC604 Machine Learning 20 20 20 80 3 -- -- 100
CSDLO6
01X Department Level Optional
Course -2 20 20 20 80 3 -- -- 100
CSL601 Data Analytics and
Visualization Lab -- -- -- -- -- 25 25 50
CSL602 Cryptography & System
Security Lab -- -- -- -- -- 25 -- 25
CSL603 Software Engineering and
Project Management Lab -- -- -- -- -- 25 - 25
CSL604 Machine Learning Lab 25 25 50
CSL605 Skill base Lab Course:
Cloud Computing -- -- -- -- -- 50 25 75
CSM601 Mini Project Lab: 2B -- -- -- -- -- 25 25 50
Total -- -- 100 400 -- 175 100 775

Page 10

PROGRAM STRUCTURE FOR THIRD YEAR
UNIVERSITY OF MUMBAI (With Effect from 2022 -2023)
DEPARTMENT OPTIONAL COURSES

Department
Optional
Courses Semester Code & Subject

Department
Optional
Course -1
V CSDLO5011 : Statistics f or Artificial Intelligence & Data Science
CSDLO5012: Advanced Algorithms
CSDLO5013: Internet of Things


Department
Optional
Course -2
VI CSDLO6011 :High Performance Computing
CSDLO6012: Distributed Computing
CSDLO6013: Image & Video processing

Page 11

Course Code Course Name Credit
CSC501 ComputerNetworks 03


Pre-requisite:None

Course Objectives: The course aims:
1 TointroduceconceptsofcomputernetworksandworkingofvariouslayersofOSI.
2 Toexploretheissuesandchallengesofprotocolsdesignwhiledelving intoTCP/IPprotocolsuite.
3 Toassessthestrengthsandweaknessesofvariousroutingalgorithms.
4 Tounderstandvarioustransportlayerandapplicationlayerprotocols
5 Todesignenterprisenetworkforgivenuserrequirementsinanapplication.

Course Outcomes:
1 Demonstrate the concepts of data communication at physical layer and compare ISO -
OSImodel withTCP/IPmodel.
2 Exploredifferentdesignissuesatdatalinklayer.
3 Design the network using IPaddressing and sub netting / supernetting schemes.
4 Analyze transport layer protocols and congestion control algorithms.
5 Explore protocols at applicationlayer
6 Understand the customer requirements andApply a Methodology to Network Design and
software defined networks


Module DetailedContent Hours
1 Introductionto Networking
1.1 Introduction tocomputernetwork,NetworkDevices,Networktopology,Switching:
Circuit -SwitchedNetworks,PacketSwitching,NetworkTypes:LAN,MAN,WAN 6
1.2 Referencemodels:LayerdetailsofOSI,TCP/IPmodels.DifferencebetweenOSI
andTCP/IP
2 Physical and Data Link Layer 10
2.1 PhysicalLayer:CommunicationmechanismsandElectromagneticSpectrum,Guide
dTransmissionMedia:Twistedpair,Coaxial,Fiberoptics
2.2 Data Link Layer: DLL Design Issues (Services, Framing, Error Control,
FlowControl), Error Detection and Correction (Hamming Code, CRC,
Checksum) ,Elementary Data Link protocols , Stop and Wait, Sliding Window
(Go Back
N,SelectiveRepeat),MediumAccessControlsublayerChannelAllocation
problem,MultipleaccessProtocol(ALOHA,CarrierSenseMultipleAccess,

Page 12

(CSMA/CD)).
3 Network Layer 7
3.1 NetworkLayer:CommunicationPrimitives,IPv4Addressing(classfulandclassless
),Subnetting, IPv4 Protocol, Network Address Translation
(NAT),IPv6addressing,IPv4vsIPv6addressing,RoutedvsRoutingprotocols, Class
ificationof Routing algorithms, Shortest Path algorithms (Dijkastra‗s),Linkstate
routing,DistanceVectorRouting
4 TransportLayer andApplicationLayer 7
4.1 Transport Layer: Service primitives, Sockets, Connectionmanagement
(Handshake),UDP,TCP, TCPstatetransition,TCPtimers,TCPFlowcontrol(slidin
gWindow)
4.2 ApplicationLayer:HTTP,SMTP,Telnet,FTP,DHCP,DNSandTypesofName
Server
5 Enterprise Network Design 5
TheCiscoServiceOrientedNetworkArchitecture,NetworkDesignMethodology,
Top-Down vs Bottom up Approach to Network Design, ClassicThree -
LayerHierarchicalModel:Core,AccessandDistributionLayers,CampusDesignCo
nsiderations,DesigningaCampusNetworkDesignTopology.
6 SoftwareDefinedNetworks 4
IntroductiontoSoftwareDefinedNetwork, Fundamental Characteristics
ofSDN,SDNBuildingBlocks,ControlandDataplanes,SDNOperation,OpenFlow
messages – Controller to Switch, Symmetric and Asynchronousmessages, SDN
OpenFlow Controllers: PoX, NoXArchitecture.


Textbooks:
1 A.S.Tanenbaum,ComputerNetworks,4th editionPearsonEducation
2 B.A. Forouzan, Data Communications and Networking, 5 th edition,TMH
3 JamesF.Kurose,KeithW.Ross,ComputerNetworking,ATop -
DownApproachFeaturingtheInternet,6thedition,AddisonWesley
4
BehrouzA.Forouzan,ForouzanMosharrat,Computer NetworksATopdownApproach,McGraw
Hill education
5 DianeTeare,AuthorizedSelf -StudyGuide,DesigningforCiscoInternetworkSolutions(DESGN),
Second Edition, Cisco Press.
6 PaulGöransson,ChuckBlack,SoftwareDefinedNetworks:AComprehensive Approach, MK
Publication
7 ThomasD.NadeauandKenGray,SoftwareDefinedNetworks,1stEdition,O‘Reillypublication

Page 13

References:
1 S.Keshav,AnEngineeringApproachToComputer Networking, Pearson.
2 NataliaOlifer&VictorOlifer,ComputerNetworks:Principles,Technologies&Protocols for
NetworkDesign,WileyIndia,2011
3 Larry L.Peterson, Bruce S.Davie, Computer Networks:ASystemsApproach, Second Edition
TheMorganKaufmannSeriesin Networking
4 SiamakAzodolmolky,SoftwareDefinedNetworking withOpen Flow :PACKTPublishing.
5 Priscilla Oppenheimer,Top -DownNetworkDesign(NetworkingTechnology)3rdEdition,
Cisco Press Book

Assessment:

InternalAssessment:
Assessmentconsistsoftwoclasstestsof20markseach.Thefirst -classtestistobeconductedwhen
approx.40%syllabusiscompletedandsecondclasstest whenadditional40%syllabusiscompleted.Durati
onof each test shall be one hour.

End SemesterTheory 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.



Useful Links
1 https://nptel.ac.in/courses/106 105183
2 https://www.coursera.org/specializations/ computer -communications
3 https://www .coursera.org/learn/t cpip?action=enroll

Page 14

Course Code Course Name Credit
CSC502 WebComputing 03


Pre-requisite:

Course Objectives: The course aims:
1 ToorientstudentstoWebProgrammingfundamental.
2 ToexposestudentstoJavaScripttodevelopinteractivewebpage development
3 ToorientstudentstoBasicsofREACTalongwithinstallation
4 Toexposestudentstonode.jsapplicationsusingexpressframework
5 ToorientstudentstoFundamentalsofnode.js
6 Toexpose studentstoAdvancedconceptsinREACT

Course Outcomes:
1 Select protocols or technologies required for various web applications
2 Apply JavaScript to add functionality to web pages. .
3 Design front end application using basic React. .
4 Construct web based Node.js applications using Express
5 Design front end applications using functional components of React.
6 Design back -end applications using Node.js


Modul
e DetailedContent Hours
1 Webprogrammingfundamentals
1.1 Workingofwebbrowser,HTTPprotocol,HTTPS,DNS,TLS,XML
introduction, Json introduction, DOM, URL, URI, RESTAPI 8
2 Javascript 8
2.1 IntroductiontoJavaScript:JavaScriptlanguageconstructs,ObjectsinJavaScript -
Built in, Browser objects and DOM objects, event handling, formvalidation
and cookies.
IntroductiontoES5,ES6,DifferencebetweenES5andES6. Variables,Condition,L
oops,Functions, Events, Arrow functions, Setting CSS
StylesusingJavaScript,DOMmanipulation,ClassesandInheritance.Iteratorsand
Generators, Promise, Client -server communication, Fetch
3 ReactFundamentals 10
3.1 Installation, Installinglibraries,Folderandfilestructure,Components,Componentl
ifecycle,StateandProps,ReactRouterandSinglepageapplications, UI design,
Forms, Events,Animations, Best practices.
4 Node.js 5

Page 15

4.1 Environmentsetup,Firstapp,Asynchronous programming,Callbackconcept,Eventlo
ops,REPL,Eventemitter,Networkingmodule,Buffers,Streams,File
system,Webmodule.
5 Express 4
5.1 Introduction, Express router, REST API, Generator,
Authentication,sessions, Integrating with React
6 Advance React 4
6.1 Functional components - Refs, Use effects, Hooks, Flow
architecture,Model -
ViewControllerframework,Flux,Bundlingtheapplication.Webpack.


Textbooks:
1 RediscoveringJavaScript,MasterES6,ES7,andES8,ByVenkatSubramaniam·2018
2 Learning ReactFunctionalWeb DevelopmentwithReactandRedux,AlexBanksandEve
Porcello, O‘Reilly
3 Learning Redux, Daniel Bugl, Packt Publication
4 Learning Node.js Development,Andrew Mead, Packt Publishing
5 RESTfulWebAPIDesignwithNode.js10,ValentinBojinov,PacktPublication

References:
1 ―WebDevelopmentwithNodeandExpress,EthanBrown,O‘Reilly
2 HTML5 Cookbook, By Christopher Schmitt, Kyle Simpson, O'Reilly Media
3 CorePythonApplications Programming byWesley JChunThird edition Pearson Publication

Assessment:

Internal Assessment:
Assessmentconsistsoftwoclasstestsof20markseach.Thefirst -classtestistobeconductedwhen
approx.40%syllabusiscompletedandsecondclasstestwhenadditional40%syllabusiscompleted.Durationof
each test shall be one hour.

End SemesterTheory 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.


Useful Links
1 https://www.coursera.org/learn/html -css-javascript -for-web-developers?action=enroll
2 ttps://onlinecourses.swayam2.ac.in/ugc19_lb05/preview
3 https://reactjs.org/tutorial/tutorial.html
4 https://react -redux.js.org/introduction/quick -start4.https://webpack.js.org/

Page 16

Course Code Course Name Credit
CSC503 ArtificialIntelligence 03


Pre-requisite:CProgramming

Course Objectives: The course aims:
1 Togainperspective ofAI and its foundations.
2 Tostudydifferentagentarchitecturesandpropertiesofthe environment
3 TounderstandthebasicprinciplesofAItowardsproblemsolving,inference,perception,
knowledge representation, and learning.
4 Toinvestigateprobabilisticreasoningunderuncertainandincompleteinformation.
5 Toexplorethecurrentscope,potential, limitations,andimplicationsofintelligentsystems

Course Outcomes:
Aftersuccessful completion of the course students will be able to:
1 Identifythe characteristicsof theenvironment anddifferentiate between variousagent
architectures.
2 Apply the most suitable search strategy to design problem solving agents.
3 Represent a natural language description of statements in logic and apply the inference rules
to design Knowledge Based agents.
4 Applyaprobabilisticmodelforreasoningunderuncertainty.
5 Comprehend various learning techniques.
6 Describe the various building blocks of an expert system for a given real word problem.


Module Detailed Content Hours
1 IntroductiontoArtificialIntelligence 3
1.1 Artificial Intelligence (AI),AI Perspectives:Acting andThinking
humanly,Actingand Thinking rationally
1.2 History ofAI,Applications ofAI,The present state ofAI, Ethics inAI
2 IntelligentAgents 4
2.1 Introductionofagents,StructureofIntelligentAgent,CharacteristicsofIntelligent
Agents
2.2 Types of Agents: Simple Reflex, Model Based, Goal Based, Utility
BasedAgent s.
2.2 Environment Types: Deterministic, Stochastic, Static,
Dynamic, Observable, Semi -observable, SingleAgent,
MultiAgent
3 SolvingProblemsbySearching 12
3.1 Definition,Statespacerepresentation,Problemasastatespacesearch,
Problemformulation,Well -definedproblems
3.2 SolvingProblemsbySearching,Performanceevaluationofsearchstrategies,Time
Complexity,SpaceComplexity,Completeness,Optimality

Page 17

3.3 Uninformed Search: Depth First Search, Breadth First Search, Depth
LimitedSearch,IterativeDeepeningSearch,UniformCostSearch,BidirectionalSe
arch
3.4 Informed Search: Heuristic Function, Admissible Heuristic, Informed
SearchTechnique, Greedy Best First Search, A* Search, Local Search: Hill
ClimbingSearch, SimulatedAnnealing Search, Optimization:
GeneticAlgorithm
3.5 GamePlaying,AdversarialSearchTechniques,Mini -maxSearch,Alpha -
BetaPruning
4 Knowledge and Reasoning 10
4.1 Definition and importance of Knowledge, Issues in Knowledge
Representation,KnowledgeRepresentationSystems,PropertiesofKnowledgeRe
presentation Systems
4.2 Propositional Logic (PL): Syntax, Semantics, Formal logic -connectives,
truthtables,tautology,validity,well -formed -formula, Introductiontologic
programming (PROLOG)
4.3 Predicate Logic: FOPL, Syntax, Semantics, Quantification, Inference rules in
FOPL,
4.4 Forward Chaining, Backward Chaining and Resolution in FOPL
5 Reasoning UnderUncertainty 5
HandlingUncertain Knowledge,RandomVariables,PriorandPosteriorPro
bability,Inference usingFull JointDistribution
Bayes' Rule and its use, Bayesian Belief Networks, Reasoning in Belief
Networks
6 Planning and Learning 5
6.1 The planning problem, Partial order planning, total order planning.
6.2 Learning inAI, LearningAgent, Concepts of Supervised, Unsupervised, Semi
-Supervised Learning, Reinforcement Learning, Ensemble Learning.
6.3 ExpertSystems,ComponentsofExpertSystem:Knowledgebase,Inferenceengi
ne,userinterface,workingmemory,DevelopmentofExpertSystems

Total 39

Textbooks:
1 Stuart J. Russell and Peter Norvig, "Artificial IntelligenceAModernApproach ―Second
Edition" Pearson Education.
2 ElaineRichandKevinKnight―ArtificialIntelligenceǁ ThirdEdition,TataMcGraw -Hill
Education Pvt. Ltd., 2008.
3 GeorgeF Luger―Artificial Intelligence‖Low PriceEdition, Pearson Education.,Fourth
edition.

References:
1 Ivan Bratko ―PROLOG Programming forArtificial Intelligence‖, Pearson Education,Third
Edition.
2 D.W.Patterson,Artificial Intelligence and Expert Systems, Prentice Hall.
3 Saroj Kaushik ―Artificial Intelligence‖, Cengage Learning.
4 DavisE. Goldberg,―GeneticAlgorithms:Search, Optimizationand MachineLearning‖,Addison
Wesley, N.Y.,1989.
5 PatrickHenryWinston,―ArtificialIntelligence‖,Addison -Wesley, ThirdEdition.
6 N.P.Padhy,―ArtificialIntelligenceandIntelligentSystems‖,OxfordUniversityPress.

Page 18

Assessment:

InternalAssessment:
Assessmentconsistsoftwoclasstestsof20markseach.The first-classtestistobeconducted
whenapprox.40%syllabusiscompletedandsecondclasstestwhenadditional40%syllabusiscompleted.Dura
tion of each test shall beone hour.

End SemesterTheory 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.



Useful Links
1 An Introduction toArtificial Intelligence - Course (nptel.ac.in)
2 NPTEL
3 https://www.classcentral.com/course/independent -elements -of-ai-12469
4 https://tinyurl.com/ai -for-everyone

Page 19

Course Code Course Name Credit
CSC504 Data Warehousing and Mining 03


Pre-requisite:DatabaseManagementconcepts

Course Objectives: The course aims:
1 Tocreateawarenessofhowenterprisecanorganizeandanalyzelargeamountsofdataby
creatingaDataWarehouse
2 TointroducetheconceptofdataMiningasanimportant toolforenterprisedatamanagementand as a
cutting edge technology for building competitive advantage.
3 Toenablestudentstoeffectivelyidentifysourcesofdataandprocessitfordatamining
4 Tomakestudentswellversedinalldataminingalgorithms,methodsofevaluation
5 Toimpartknowledgeoftoolsusedfordatamining,andstudywebmining

Course Outcomes:
1 OrganizestrategicdatainanenterpriseandbuildadataWarehouse.
2 Analyze data using OLAPoperations so as to take strategic decisions andDemonstrate an
understanding of the importance of data mining.
3 Organizeand Preparethe data neededfor data miningusing prepreprocessing techniques
4 Implement the appropriate data mining methods like classification, clustering or Frequent
Patternminingonlargedata sets.
5 Define and apply metrics to measure the performance of various data mining algorithms
6 UnderstandConceptsrelatedtoWebmining


Modul
e DetailedContent Hours
1 Data Ware house and OLAP
DataWarehousing, Dimensional ModelingandOLAPThe
NeedforDataWarehousing; Data Warehouse Defined; Benefits of Data
Warehousing ; Features of a DataWarehouse;
DataWarehouseArchitecture;Data
WarehouseandDataMarts;DataWarehousingDesignStrategies.
Dimensional Model Vs ER Model; The Star Schema, The
SnowflakeSchema;FactTablesandDimension Tables;FactlessFactTable;U
pdatesToDimensionTables,PrimaryKeys,SurrogateKeys&ForeignKeys;
AggregateTables;FactConstellationSchemaorFamiliesofStarNeedfor Onlin
eAnalyticalProcessing; OLTPvsOLAP; OLAPOperations ina
cube:Roll -up,Drilldown,Slice,Dice,Pivot;OLAPMo dels:MOLAP,
ROLAP,HOLAP.MajorstepsinETLProcess 9
2 IntroductiontoDataMining,DataExplorationandDataPreprocessing 8

Page 20

DataMiningTaskprimitives,Architecture,KDDprocess,IssuesindataMining,Typ
esofAttributes;StatisticalDescriptionofData;Data Visualization;Measuringsimil
arityand dissimilarity. Why Preprocessing?Data Cleaning; Data Integration;
Data Reduction: Attribute subset
selection,Histograms,ClusteringandSampling;DataTransformation&DataDiscr
etization:Normalization,Binning,HistogramAnalysis andConcept
hierarchy generation.
3 Classification 6
Basic Concepts; Classification methods: 1. Decision Tree Induction:
AttributeSelection Measures, Tree pruning. 2. Bayesian Classification: Naïve
Bayes‟Classifier.Prediction:Structureofregression models;Simplelinearregressi
on,Multiplelinearregression.AccuracyandErrormeasures,
Precision, Recall
4 Clustering 4
ClusterAnalysis:BasicConcepts;PartitioningMethods:K -
Means,KMediods;HierarchicalMethods:Agglomerative,Divisive,BIRCH;Dens
ity-Based Methods: DBSCAN What are outliers? Types, Challenges;Outlier
Detection Methods: Supervised, Semi Supervised, Unsupervised,Proximity
based, Clustering Based
5 FrequentPattern 8
Market Basket Analysis, Frequent Itemsets, Closed Itemsets, and Association Rules;
Frequent Pattern Mining, Efficient and Scalable Frequent Itemset Mining Methods,
The Apriori Algorithm for finding Frequent Itemsets Using Candidate Generation,
Generating Association Rules from Frequent Itemsets, Improving the Efficienc y of
Apriori, A pattern growth approach for mining Frequent Itemsets; Mining Frequent
itemsets using vertical data formats; Introduction to Mining Multilevel Association
Rules and Multidimensional Association Rules; From Association Mining to
Correlation A nalysis, lift, ; Introduction to Constraint -Based Association Mining
6 WebMining 4
Introduction toWebcontentMining, Crawlers, Personalization,
Webstructuremining, Pagerank,, Clever, WebUsageMining


Textbooks:
1 Han,Kamber,"DataMiningConceptsand Techniques",MorganKaufmann3ndEdition
2 P.N.Tan,M.Steinbach,VipinKumar,―IntroductiontoDataMining‖,PearsonEducation.
3 PaulrajPonniah,―DataWarehousing:FundamentalsforITProfessionals‖,WileyIndia.
4 Raghu Ramakrishnan and Johannes Gehrke, ―Database Management Systems‖ 3rd Edition -
McGraw Hill
5 Elmasri and Navathe, ―Fundamentals of Database Systems‖, 6th Edition, PEARSON
Education

References:
1 TherajaReema,―DataWarehousing‖,OxfordUniversityPress,2009
2 RalphKimball,MargyRoss,―TheDataWarehouse Toolkit:TheDefinitiveGuideTo
DimensionalModeling‖,3rdEdition.WileyIndia.

Page 21

3 MichaelBerryandGordonLinoff―MasteringDataMining -Art&scienceofCRM‖,Wiley
Student Edition
4 MichaelBerryandGordonLinoff―DataMiningTechniques‖,2ndEditionWiley Publications

Assessment:

InternalAssessment:
Assessmentconsistsoftwoclasstestsof20markseach.Thefirst -
classtestistobeconductedwhenapprox.40%syllabusiscompletedand second class test when
additional40% syllabus is completed.
Durationofeachtestshallbeone hour.

End SemesterTheory 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.



Useful Links
1 https://www.coursera.org/learn/data -warehousing -business -intelligence
2 https://www.coursera.org/specializations/data -mining -foundations -practice
3 https://onlinecourse s.nptel.ac.in/noc20_cs12/preview
4 https://nptel.ac.in/courses/106105174

Page 22

Course Code Course Name Credit
CSDLO5011 Statistics for Artificial Intelligence Data Science 03


Prerequisite:C Programming

Course Objectives: The course aims:
1 ToPerformexploratoryanalysisonthedatasets
2 ToUnderstandthevariousdistributionandsampling
3 ToPerformHypothesisTestingondatasets
4 ToExploredifferenttechniquesforSummarizingData
5 ToPerformThe Analysis ofVariance
6 ToExploreLinearLeastSquares
Course Outcomes: Learner will be able to
1 Illustrate Exploratory DataAnalysis
2 Describe Data and Sampling Distributions
3 SolveStatisticalExperimentsandSignificanceTesting
4 Demonstrate Summarizing Data
5 InterprettheAnalysisofVariance
6 Use Linear Least Squares

Prerequisite: DiscreteStructuresandGraphTheory

Module DetailedContent Hours
1 Exploratory DataAnalysis 5
1.1 ElementsofStructuredData,Further Reading ,Rectangular Data ,Data Frames andIndexes
,Nonrectangular Data Structures , Estimates of Location ,Mean ,Median
andRobustEstimates,EstimatesofVariability,StandardDeviationandRelatedEstimates
,EstimatesBasedonPercentiles,ExploringtheDataDistribution,PercentilesandBoxplots,Fre
quencyTables andHistograms,DensityPlotsandEstimates.
1.2 Exploring Binary and Categorical Data , Mode ,Expected Value, Probability
,Correlation,Scatterplots,ExploringTwoorMoreVariables,HexagonalBinningandContour
s(PlottingNumericVersusNumericalData),Two CategoricalVariables
,CategoricalandNumeric Data,VisualizingMultipleVariables.
2 DataandSamplingDistributions 6
2.1 Random Sampling and
SampleBias,Bias,RandomSelection,SizeVersusQuality,SampleMeanVersusP
opulationMean,SelectionBias,RegressiontotheMean
,SamplingDistributionofaStatistic,CentralLimitTheorem,StandardError,TheBootstrap,R
esamplingVersusBootstrapping.
2.2 Confidence Intervals ,Normal Distribution ,Standard Normal and QQ-Plots
,Long -TailedDistributions,Student‘st -Distribution,Binomial Distribution,Chi -
SquareDistribution,F -Distribution,PoissonandRelatedDistributions,PoissonDistributions
,ExponentialDistribution,EstimatingtheFailureRate,WeibullDistribution.

SelfStudy: Problemsindistributions.
3 Statistical ExperimentsandSignificance Testing 8
3.1 A/B Testing ,Hypothesis Tests ,The Null Hypothesis ,Alternative Hypothesis ,One -
WayVersusTwo -WayHypothesisTests,Resampling,PermutationTest,Example:Web
Stickiness,Exhaustive and Bootstrap Permutation Tests ,Permutation Tests: The
BottomLine forDataScience,StatisticalSignificanceandp -Values,p -

Page 23

Value,Alpha,Type1and

Page 24

Type2Errors
3.2 DataScienceandp -Values,t -Tests,MultipleTesting,DegreesofFreedom,ANOVA
,F-Statistic,Two -Way ANOVA , Chi -Square Test ,Chi -Square Test: A
Resampling Approach ,Chi -Square Test: Statistical Theory,Fisher‘s Exact Test
,Relevance for Data Science ,Multi -Arm BanditAlgorithm ,Powerand Sample Size
,Sample Size .

SelfStudy: TestingofHypothesisusinganystatisticaltool
4 SummarizingData 6
4.1 Methods Based on the Cumulative Distribution Function , The Empirical
CumulativeDistribution Function ,The Survival Function ,Quantile -Quantile Plots ,
Histograms,DensityCurves,andStem -and-LeafPlots,MeasuresofLocation.
4.2 TheArithmeticMean,TheMedian,TheTrimmedMean,M Estimates,Comparisonof
LocationEstimates,EstimatingVariabilityofLocationEstimatesbytheBootstrap,Measureso
fDispersion,Boxplots,ExploringRelationshipswithScatterplots.

SelfStudy: usinganystatisticaltoolperformdatasummarization
5 TheAnalysisofVariance 6
5.1 TheOne -WayLayout,NormalTheory;theFTest,TheProblemofMultipleComparisons , A
Nonparametric Method —The Kruskal -Wallis Test ,The Two -
WayLayout,AdditiveParametrization,NormalTheoryfortheTwo -WayLayout
,RandomizedBlockDesigns ,ANonparametricMethod —Friedman‘sT est.
6 LinearLeastSquares 8
6.1 Simple Linear Regression, Statistical Properties of the Estimated Slope and Intercept
,Assessing the Fit , Correlation and Regression , The Matrix Approach to Linear
LeastSquares , Statistical Properties of Least Squares Estimates , Vector -Valued
RandomVariables,MeanandCovarianceofLeastSquaresEstimates,Estimationofσ2,Residu
alsandStandardizedResiduals,Inferenceaboutβ,MultipleLinearRegression —
AnExample,Conditional Inference, Unconditional Inference, and
theBootstrap, LocalLinearSmoothing.

Self Study : Create a Linear Regression model for a dataset and display the
errormeasures,Choseadatasetwithcategoricaldataand apply linear
regressionmodel


Textbooks:
1 Bruce, Peter, and Andrew Bruce. Practical statistics for data scientists: 50 essential concepts. Reilly
Media,2017.
2 Mathematical Statistics and Data Analysis John A. Rice University of California, Berkeley,Thomson Higher
Education
References:
1 Dodge, Yadolah,ed.Statisticaldataanalysisand inference.Elsevier,2014.
2 Ismay, Chester, and Albert Y. Kim. Statistical Inference via Data Science: A Modern Dive into R and
theTidyverse.CRCPress,2019.
3 Milton. J. S. andArnold. J.C., "Introduction to Probability and Statistics",Tata McGraw Hill, 4th Edition,
2007.
4 Johnson.R.A.andGupta.C.B.,"MillerandFreund‘sProbabilityandStatisticsforEngineers",Pearson
Education,Asia, 7th Edition, 2007.
5 A.Chandrasekaran,G.Kavitha,―Probability,Statistics,RandomProcessesand QueuingTheory‖,Dhanam
Publications,2014.

Page 25

InternalAssessment: Assessment :

Assessment consists of two class tests of 20 marks each. The first -class test is to be conducted when approx.
40%syllabusiscompletedandsecondclasstestwhen additional40%syllabusiscompleted.Durationofeachtestshallbe
onehour.
EndSemesterTheoryExamination:
1 Questionpaperwillconsistof6questions,eachcarrying20marks.
2 Thestudentsneedtosolveatotalof4questions.
3 QuestionNo.1willbecompulsoryandbasedontheentire syllabus.
4 Remainingquestion(Q.2toQ.6)willbeselectedfromallthemodules.



UsefulLinks
1 https:// www.edx.org/course/introduction -probability -science -mitx-6-041x -2
2 https:// www.coursera.org/learn/statistical -inference
3 https:// www.datacamp.com/community/open -courses/statistical -inference -and-data-analysis

*Suggestion:LaboratoryworkbasedontheabovesyllabuscanbeincorporatedasaminiprojectinCSM501:M
ini-Project.

Page 26

Course Code Course Name Credit
CSDL05012 Advanced Algorithms 03

Pre-requisite:

Course Objectives: The course aims:
1 ToprovidemathematicalapproachesforproblemsolvingusingadvancedconceptsofAlgorithms
2 TounderstandandsolveproblemsusingvariousalgorithmicapproacheslikeRandomizedalgorithms,
approximation algorithms, Local search and Amortized algorithms.
3 TodiscussandapplytheCombinatorialAnalysistechniquestosolvevariousmathematicalandstatisti
cal problems

Course Outcomes:
1 AnalyzetheclassificationofproblemsintovariousNPclassesandtheir ComputationalIntractability
2 Describe, apply and analyze the complexity of Approximation Algorithms.
3 Describe, apply and analyze the complexity of RandomizedAlgorithms.
4 Describe,applyandanalyzethecomplexityofLocalSearchAlgorithms.
5 Design and Apply the concepts ofString andAmortizedAnalysis
6 To Understand CombinatorialAnalysistechniques


Module DetailedContent Hours
1 NPand Computational Intractability
1.1 Polynomial -TimeReductions,NPCompleteness:Overview,ClassP –ClassNP
– NP Hardness, NP Completeness, Cook Levine Theorem, Characteristics
ofNP Complete Problems, The Satisfiability Problem, NP -Complete
Problems,SequencingProblemsPartitioningProblems,GraphColoring,Numerical
Problems, Co -NP and the Asymmetry of NP, A Partial Taxonomy of
HardProblems. Reduction of standard NP Complete Problems: SAT, 3SAT,
Clique,VertexCover,SetCover,Hamiltonian Cycle. 8
2 Approximation Algorithms 9

Page 27

2.1 Approximation algorithms for known NP hard problems,
Inapproximability,Approximation algorithmswithsmalladditiveerror:EdgeColor
ing,BinPacking,Randomizedroundingandlinearprogramming,Problemshavingp
olynomialapproximationschemes,Optimizationproblemswithconstant -
factorapproximations,Hard -to-
approximateproblems,AnalysisofApproximationAlgorithms .
3 RandomizedAlgorithms 9
3.1 Introductiontorandomizedalgorithm,FindingtheGlobalMinimumCut,RandomV
ariablesandTheirExpectations,ARandomizedApproximationAlgorithmforMAX
3-SAT,RandomizedDivideandConquer:Median -Finding and Quicksort,
Hashing: A Randomized Implementation ofDictionaries,FindingtheClosest
Pair of Points: A Randomized Approach,Randomized Caching, Chernoff
Bounds, Load Balancing, Packet Routing, LasVegasAlgorithm,
MonteCarloAlgorithm.
4 LocalSearch 5
4.1 TheLandscapeofanOptimization Problem,TheMetropolis Algorithm
andSimulatedAnnealing,AnApplicationofLocalSearchtoHopfieldNeuralNetwor
ks,Maximum -CutApproximationviaLocalSearch,ChoosingaNeighbour
Relation, Classification via Local Search, Best -Response Dynamicsand Nash
Equilibria.
5 String andAmortizedAnalysis 4
5.1 String Sort, Tries, Substring Search, Regular Expressions, Data
Compression,StringMatchingAlgorithms:IntroductiontoStringmatching,TheKn
uth-Morris -Pratt algorithm, Aho - Korasik algorithm, Z -algorithm,
AmortizedAnalysis: Aggregateanalysis,Theaccountingmethod, The potential
methodDynamic tables.
6 CombinatorialAnalysis 4
6.1 Introduction, Next subset of n -Set problems, Random Subset of n -
Setproblems, Sequencing, Ranking and selection algorithms for
generalcombinatorial families.



Textbooks:
1 JonKleinberg,EvaTardos,―AlgorithmDesign‖,CornellUniversity,PearsonPublications
2 RobertSedgewick,KevinWayne,―Algorithms‖,Princeton,FOURTHEDITION,AddisonWess
ely.

Page 28

3 ThomasH.Cormen,CharlesE.,Ronald
l.,Clifford Stein,―IntroductiontoAlgorithms‖,Thi
rd Edition, The MITPress Cambridge.
4 AlbertNijenhuis,HerbertWilf,―CombinatorialAlgorithmsforcomputersandcalculators‖,Second
edition,Academic Press
5 GeorgeHeineman,GaryPollice,StanleySelkow,―AlgorithmsinaNutshell‖, OreillyPress.

References:
1 AnanyLevitin,Introduction toThe designand analysisof algorithms,3rdEdition,Pearson
publication.
2 Peter J.Cameron, ―Combinatorics: Topics,Techniques,Algorithms‖, CambridgeUniversity
Press

Assessment:

InternalAssessment:
Assessmentconsistsoftwoclasstestsof20markseach.Thefirst -
classtestistobeconductedwhenapprox.40%syllabusiscompletedand second class test when additional40%
syllabus is completed.
Durationofeachtestshallbeonehour.

End SemesterTheory 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.



Useful Links
1 https://www.binghamton.edu/watson/continuing -education/data -science/advanced -algorithms
.html
2
https://nptel.ac.in/courses/106104019
3
https://www.coursera.org/learn/advanced -algorithms -and-complexity
4
https://onlinecourses.swayam2.ac.in/cec20_cs03/preview


*Suggestion:LaboratoryworkbasedontheabovesyllabuscanbeincorporatedasaminiprojectinC
SM501:Mini -Project.

Page 29

Course Code Course Name Credit
CSDLO5013 Internetof Things
03


CourseObjectives: Tounderstand Internet of Things (IoT)CharacteristicsandConceptualFramework
1. TocomprehendCharacteristicsandConceptualFrameworkofIoT
2. TounderstandlevelsoftheIoTarchitectures
3. TocorrelatetheconnectionofsmartobjectsandIoTaccesstechnologies
4. ToInterpretedgetocloudprot ocols
5. ToexploredataanalyticsanddatavisualizationonIoTData
6. ToexploreIoTapplications

CourseOutcomes: Learnerwillbeableto
1. Describe theCharacteristicsand Conceptual Framework of IoT
2. Differentiatebetweenthe levelsofthe IoTarchitectures
3. Analyze the IoTaccess tec hnologies
4. Illustrate various edge to cloud protocol for IoT
5. Apply IoTanalytics and data visualization
6. Analyze and evaluate IoTapplications

Prerequisite:
1. Pythonprogramming
2. Cprograminglanguage
3. ComputerNetworks
DETAILED SYLLABUS:

Sr.
No. Module Detailed Conten
t Hou
rs
1 Introductiont
oIoT IntroductiontoIoT -
DefiningIoT,CharacteristicsofIoT,ConceptualFramework of IoT,
Physical design of IoT, Logical design of IoT, Functionalblocks
of IoT, Brief review of applications of IoT. Smart Object –
Definition, CharacteristicsandTrends
Self-learning Topics: Hardware and software development
tools for -Arduino,NodeMCU,ESP32,RaspberryPi, for
implementing internet ofthings,Simulators -
Circuit.io,Eagle,Tinkercad 4

Page 30

2 IoT
Architecture DriversBehindNewNetwork Architectures :Scale,Security,Const
rained
DevicesandNetworks,Data,LegacyDeviceSupport
Architecture: TheIoTWorldForum(IoTWF)StandardizedArchitec
ture
:Layer1 -
7,ITandOTResponsibilitiesintheIoTReferenceModel,AdditionalI
oTReferenceModels
ASimplifiedIoT Architecture
TheCoreIoTFunctionalStack::Layer1 -
3,AnalyticsVersusControlApplications,DataVersusNetworkAnal
yticsDataAnalyticsVersusBusinessBenefits,SmartServices,
IoTDataManagementandComputeStack :FogComputing,Edge
Computing,TheHierarchyofEdge,Fog,andCloud
Self-learning Topics: Briefreview of applications of IoT:
ConnectedRoadways,ConnectedFactory,SmartConnectedBuildin
gs,SmartCreaturesetc, 7
3 Principlesof
ConnectedDev
ices
andProtocolsi
nIoT
RFID and NFC (Near -Field Communication), Bluetooth Low
Energy
(BLE)roles,LiFi,WPANstd:802.15standards:Bluetooth,IEEE802
.15.4,Zigbee,Z -
wave,NarrowBandIoT,InternetProtocolandTransmissionControl
Protocol,6LoWPAN,WLANandWAN,IEEE802.11,Long -
rangeCommunication Systems and Protocols: Cellular
Connectivity -LTE, LTE -A,LoRaandL oRaWAN. 8
4 EdgetoCloud
Protocol
HTTP,WebSocket,Platforms.HTTP -MQTT -
.ComplexFlows:IoTPatterns:Real -timeClients, MQTT, MQTT -
SN, Constrained ApplicationProtocol (CoAP), Streaming Text
Oriented Message Protocol
(STOMP),AdvancedMessageQueuingProtocol(AMQP), Compari
sonofProtocols. 8
5 IoTandData
Analytics Defining IoT Analytics, IoT Analytics challenges, IoT analytics
for the cloud,Strategies to organize Data for IoT Analytics,
Linked Analytics Data
Sets,ManagingDatalakes,Thedataretentionstrategy,visualization a
ndDashboarding -
DesigningvisualanalysisforIoTdata,creatingadashboard
,creatingandvisualizingalerts.
Self-learningTopics: AWSandHadoopTechnology 7
6 IoTApplicatio
nDesign
Prototyping for IoT and M2M, Case study related to : Home
Automation(Smartlighting, Homeintrusiondetection),Cities(Smar
tParking),Environment(Weathermonitoring,weatherreportingBot
,Airpollutionmonitoring,Forestfiredetection,Agriculture(Smartirr
igation),SmartLibrary.IntroductiontoI -IoT,UsecasesoftheI -
IoT,IoTandI -IoT–
similaritiesanddifference s,IntroductiontoInternetofBehavior(IoB
)
5

Page 31

Self-learning Topics: Internet of Behaviors (IoB) and its role in
customerservices

Page 32

TextBook

1. ArsheepBahga (Author), VijayMadisetti ,InternetOfThings:AHands -
OnApproachPaperback,UniversitiesPress,Reprint2020
2. DavidHanes ,GonzaloSalgueiro ,PatrickGrossetete ,RobertBarton ,JeromeHenry ,IoTFunda
mentalsNetworkingTechnologies,Protocols,andUseCasesfortheInternetofThingsCISCO.
3. AnalyticsfortheInternetofThings(IoT)IntelligentAnalyticsforYourIntelligentDevices.Andr
ewMinteer,Packet
4. GiacomoVeneri,AntonioCapasso,‖Hands -
OnIndustrialInternetofThings:CreateapowerfulIndustrialIoTinfrastructureusingIndustry4.
0‖,Packt


References:
1. PethuruRaj ,AnupamaC.Raman ,TheInternetofThings:EnablingTechnologies,Platforms,an
dUseCasesby,CRCpress,
2. Raj Kamal, Internet ofThings,Architecture and DesignPrinciples, McGraw Hill Education,
Reprint2018.
3. Perry Lea, Internet of Things for Architects: Architecting IoT solutions by implementing
sensors,
communicationinfrastructure,edgecomputing,analytics,andsecurity,PacktPublications,Rep
rint2018.
4. Amita Kapoor, ―Hands onArtificial intelligence for IoT‖, 1st Edition, Packt Publishing,
2019.
5. Sheng -
LungPeng,SouvikPal,Lianfen HuangEditors:PrinciplesofInternetofThings(IoT)Ecosystem:
InsightParadigm,Springer


OnlineReferences:
1. https://owasp.org/www -project -internet -of-things/
2. NPTEL:SudipMisra,IITKhargpur, IntroductiontoIoT:Part -
1,https://nptel.ac.in/courses/106/105/106105166/
3. NPTEL: Prof.Prabhakar,IIScBangalore,DesignforInternet ofThings,
https://onlinecourses.nptel.ac.in/noc21_ee85/previe w
4. Mohd Javaid, Abid Haleem, Ravi Pratap Singh, Shanay Rab, Rajiv Suman,Internet of
Behaviors(IoB)anditsroleincustomerservices,SensorsInternational,Volume2,2021,100122
,ISSN2666 -3511,https://doi.org/10.1016/j.sintl.2021.100122


* Suggestion:Laboratoryworkbase dontheabovesyllabuscanbeincorporatedas
aminiprojectinCSM501:Mini -Project.

Page 33

Lab Code Lab Name Credit
CSL501 Web Computingand Network Lab 1


Prerequisite:OperatingSystem,BasicsofJavaandPythonProgramming.
Lab Objectives:
1 ToorientstudentstoHTML formakingwebpages
2 ToexposestudentstoCSSforformattingwebpages
3 Toexposestudentstodevelopingresponsivelayout
4 ToexposestudentstoJavaScripttomakewebpagesinteractive
5 ToorientstudentstoReactfordevelopingfrontendapplications
6 Toorientstudentsto Node.jsfordevelopingbackendapplications
Lab Outcomes:
1 Identify and apply the appropriate HTMLtags to develop a webpage
2 Identify and apply the appropriate CSS tags to format data on webpage
3 Construct responsive websites using Bootstrap
4 Use JavaScript to develop interactive web pages.
5 Construct front end applications using React and back end using Node.js/express
6 Use simulator for CISco packet tracer/GNS3


Suggested Experiments: Students are required to complete at least 10 experiments.
Star(*)markedexperimentsarecompulsory.
Sr.No. Name of the Experiment
1* HTML:Elements,Attributes,Head,Body, Hyperlink,Formatting, Images,Tables,
List, Frames, Forms, Multimedia
2* CSS3.Syntax,Inclusion,Color,Background,Fonts,Tables, lists,CSS3selectors,
Pseudo classes, Pseudo elements .
3 Bootstrap:BootstrapGridsystem,Forms,Button,Navbar,Breadcrumb,Jumbotron
4* Javascript:Variables,Operators,Conditions,Loops,Functions,Events,Classesand
Objects, Error handling, Validations,Arrays, String,Date
5* React:Installation and Configuration. JSX, Components, Props, State, Forms, Events,
Routers, Refs, Keys.
6* Node.Js:Installation and Configuration, Callbacks, Event loops, Creating express app
7* Todesignandsimulatetheenvironmentfor DynamicroutingusingCiscopackettracer/
GNS3
8* TodesignandSimulateVLANsontheswitch/routerusingCiscopackettracer/GNS3

Page 34

9* TodesignandSimulateNATontherouterusingCiscopackettracer/GNS3
10* Simulation of Software Defined Network using Mininet

Useful Links:
1 www.leetcode.com
2 www.hackerrank.com
3 www.cs.usfca.edu/~galles/visualization/Algorithms.html
4 www.codechef.com

TermWork:
1 Termworkshouldconsistof10experimentsfromabovelist.
2 Journal must include at least 2 assignments.
3 The final certification and acceptance of term work ensures that satisfactory performance
oflaboratory work and minimum passing marks in term work.
4 Total 25Marks(Experiments:15 -marks,AttendanceTheory&Practical:05 -marks,
Assignments: 05 -marks)
Oral & Practical exam
Based on the entire syllabus of CSL501and CSC502

Page 35

Lab Code Lab Name Credit
CSL502 ArtificialIntelligence Lab 1


Prerequisite:CProgrammingLanguage.
Lab Objectives:
1 TodesignsuitableAgentArchitectureforagivenrealworldAIproblem
2 To implement knowledge representationandreasoning inAIlanguage
3 TodesignaProblem -Solving Agent
4 To incorporate reasoningunder uncertaintyfor anAIagent
Lab Outcomes:
At the end of the course, students will be able to —-
1 Identify suitableAgent Architecture for a given real worldAI problem
2 Implement simple programs using Prolog.
3 Implement various search techniques for a Problem -SolvingAgent.
4 Represent natural language description as statements in Logic and apply inference rules to it.
5 Construct a Bayesian Belief Network for a given problem and draw probabilistic inferences
from it



Suggested Experiments: Students are required to complete at least 10 experiments.

Sr.No. Name of the Experiment
1 Provide thePEASdescriptionandTASK EnvironmentforagivenAIproblem.
2 Identify suitableAgentArchitecture for the problem
3 WritesimpleprogramsusingPROLOGasanAIprogrammingLanguage
4 Implement any one of the Uninformed search techniques
5 Implement any one of the Informed search techniques
E.g.A -Star algorithm for 8 puzzle problem
6 Implement adversarial search using min -max algorithm.
7 Implement any one of the Local Search techniques.
E.g. Hill Climbing, SimulatedAnnealing, Genetic algorithm
8 Prove the goal sentence from the following set of statements in FOPLby applying
forward, backwardand resolution inference algorithms.
9 Create a Bayesian Network for the given Problem Statement and draw
inferencesfromit.(YoucanuseanyBeliefandDecisionNetworksToolformodelingBayes
ian
Networks)
10 Implement a PlanningAgent
11 Design a prototype of an expert system
12 Case study of any existingsuccessfulAI system

Page 36

Useful Links:
1 An Introduction toArtificial Intelligence - Course (nptel.ac.in)
2 https://tinyurl.com/ai -for-everyone
3 https://ai.google/education/
4 https://openai.com/research/

TermWork:
1 Termwork shouldconsistof10experiments.
2 Journal must include at least 2 assignments.
3 The final certification and acceptance of term work ensures that satisfactory performance
oflaboratory work and minimum passing marks in term work.
4 Total 25Marks (Experiments:15 -marks,AttendanceTheory&Practical:05 -marks,
Assignments: 05 -marks)
Oral & Practical exam
Based on the entire syllabus

Page 37

Lab Code Lab Name Credit
CSL503 Datawarehousing and Mining Lab 1


Prerequisite:JavaandPythonProgramming.
Lab Objectives:
1 Tocreateawarenessofhowenterprisecanorganizeandanalyzelargeamountsofdataby
creatingaDataWarehouse
2 TointroducetheconceptofdataMiningasanimportanttoolforenterprisedatamanagement
and as a cutting edge technology for building competitive advantage
3 Toenablestudentstoeffectivelyidentifysourcesofdataandprocessitfordatamining
4 Tomakestudentswellversedinalldataminingalgorithms,methods,andtools..
Lab Outcomes:
1 Build a data warehouse
2 Analyze data using OLAPoperations so as to take strategic decisions.
3 Demonstrate an understanding of the importance of data mining
4 Organizeand Preparethe data neededfor data miningusing prepreprocessing techniques
5 Perform exploratory analysis of the data to be used for mining.
6 Implement the appropriate data mining methods like classification, clustering or Frequent
Patternminingonlargedata sets.


Suggested Experiments: Students are required to complete all experiments from the list given
below.
Sr.No. Name of the Experiment
1 Data WarehouseConstructiona)ReallifeProblemtobedefinedforWarehouseDesign
b) Construction of star schema and snow flake schema c) ETLOperations.
2 Construction of Cubes , OLAPOperations, OLAPQueries
3 Tutorialsa)Solvingexercises inDataExplorationb) Solving exercisesinData
preprocessing
4 Using open source tools Implement Classifiers
5 Using open source tools ImplementAssociation MiningAlgorithms
6 Using open source tools ImplementClusteringAlgorithms
7 Implementationofanyoneclassifierusinglanguageslike JAVA/python
8 ImplementationofanyoneclusteringalgorithmusinglanguageslikeJAVA/python
9 ImplementationofanyoneassociationminingalgorithmusinglanguageslikeJAVA/
python .
10 Implementation of page rank algorithm.

Page 38

11 Implementation of HITS algorithm.

Useful Links:
1 www.leetcode.com
2 www.hackerrank.com
3 www.cs.usfca.edu/~galles/visualization/Algorithms.html
4 www.codechef.com

TermWork:
1 Termworkshouldconsistof10experiments.
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 25Marks(Experiments:15 -marks,AttendanceTheory&Practical:05 -marks,
Assignments: 05 -marks)
Oral & Practical exam
Based on the entire syllabus of CSL301and CSC303

Page 39

CourseCode CourseName Credit
CSL504 BusinessCommunication&EthicsII 02


Course Rationale: This curriculum is designed to build up a professional and ethical
approach,effective oral and written communication with enhanced soft skills. Through
practical sessions,
itaugmentsstudent'sinteractivecompetenceandconfidencetorespondappropriatelyandcreativelyto
theimpliedchallengesoftheglobalIndustrialandCorporaterequirements. Itfurtherinculcatesthe
socialresponsibilityo fengineers astechnical citizens.
CourseObjectives
1 Todiscernanddevelop aneffectivestyleofwritingimportanttechnical/business documents.
2 Toinvestigatepossibleresourcesandplanasuccessfuljobcampaign.
3 Tounderstandthedynamicsofprofessionalcommunication intheformofgroupdiscussions,mee
tings,etc.required forcareerenhancement.
4 Todevelopcreativeandimpactfulpresentation skills.
5 Toanalyzepersonaltraits,interests,values,aptitudesandskills.
6 Tounderstand the importance ofintegrityanddevelop a personal codeofethics.
CourseOutcomes: Attheendofthecourse, thestudent will beable to
1 Planandprepareeffectivebusiness/technicaldocumentswhichwillinturnprovidesolid
foundationfortheirfuturemanagerialroles.
2 Strategizetheirpersonalandprofessionalskills tobuilda professional imageandmeet
thedemandsoftheindustry.
3 Emergesuccessfulingroupdiscussions, meetingsandresult -orientedagreeablesolutionsin
groupcommunicationsituations.
4 Deliverpersuasiveandprofessionalpresentations.
5 Developcreativethinkingand interpersonalskillsrequiredforeffectiveprofessional
communication.
6 Applycodesofethicalconduct,personalintegrityand normsoforganizationalbehaviour.


Module Conten
ts Ho
urs
1 ADVANCEDTECHNICALWRITING:PROJECT/PROBLEM
BASEDLEARNING(PBL) 06








PurposeandClassificationofReports:
Classification on the basis of: Subject Matter (Technology,
Accounting,Finance, Marketing, etc.), Time Interval (Periodic, One -time,
Special),Function(Informational,Analytical,etc.),PhysicalFactors(Memora
ndum,Letter,Short &Long)
Parts of a Long Formal Report: Prefatory Parts (Front Matter),
ReportProper(Main Body), Appended Parts(BackMatter)
Language and Style of Reports: Tense, Person & Voice of
Reports,Numbering Style of Chapters, Sections, Figures, Tables and
Equations,R eferencingStylesinAPA&MLAFormat,
ProofreadingthroughPlagiarismCheckers
Definition, Purpose & Types of Proposals: Solicited (in conformance
withRFP)&Unsolicited Proposals,Types(Shortand Longproposals)
Partsofa Proposal: Elements,ScopeandLimitations,Conclusio nTechnical
Paper Writing: Parts of a Technical Paper (Abstract, Introduction,Research
Methods, Findings and Analysis, Discussion, Limitations,

Page 40

FutureScopeandReferences),Languageand
Formatting,ReferencinginIEEEFormat
2 EMPLOYMENTSKILLS 06
Cover Letter & Resume: Parts and Content of a Cover Letter,
Differencebetween Bio -data, Resume & CV, Essential Parts of a
Resume, Types ofResume (Chronological, Functional&Combination)
StatementofPurpose: ImportanceofSOP,TipsforWritinganEffectiveSOP
VerbalAptitudeTest: ModelledonCAT,GRE,GMATexams
GroupDiscussions: PurposeofaGD,ParametersofEvaluatingaGD,Types
of GDs (Normal, Case -based & Role Plays), GD
Etiquettes PersonalInterviews: PlanningandPreparation,TypesofQuesti
ons,TypesofInterviews(Structured,Stress,Behavioural,Probl emSolving
&Case -based),ModesofInterviews:Face -to-face(One -
tooneandPanel)Telephonic,Virtual
3 BUSINESSMEETINGS 02
ConductingBusinessMeetings: TypesofMeetings,RolesandResponsibil
itiesofChairperson,SecretaryandMembers,MeetingEtiquette
Documentation: Notice, Agenda,Minutes
4 TECHNICAL/BUSINESSPRESENTATIONS 02
Effective Presentation Strategies: Defining Purpose,
AnalyzingAudience, Location and Event, Gathering, Selecting
&ArrangingMaterial,structuringaPresentation,MakingEffectiveSlides
,TypesofPresentations Aids, ClosingaPresentation, Platformskills
GroupPresentations: SharingResponsibilityinaTeam,Buildingtheco
ntentsand visuals together, TransitionPhases
5 INTERPERSONALSKILLS 08
Interpersonal Skills: Emotional Intelligence, Leadership &
Motivation,Conflict Management & Negotiation, Time Management,
Assertiveness,DecisionMaking
Start -up Skills: Financial Literacy, Risk Assessment, Data
Analysis(e.g.ConsumerBehaviour,MarketTrends,etc.)
6 CORPORATEETHICS 02
Intellectual Property Rights: Copyrights, Trademarks,
Patents,IndustrialDesigns,GeographicalIndications,IntegratedCircuits,
TradeSecrets (UndisclosedInformation)
CaseStudies: CasesrelatedtoBusiness/CorporateEthics


Listof assignments:(In theformofShortNotes,
Questionnaire/MCQTest,RolePlay,Case Study, Quiz, etc.)
Sr.
No. TitleofExperiment
1 CoverLetterandResume
2 ShortProposal
3 MeetingDocumentation
4 WritingaTechnical Paper/AnalyzingaPublishedTechnical Paper
5 Writinga SOP
6 IPR

Page 41

7 InterpersonalSkills
Note:
1 TheMain Bodyofthe project/bookreportshouldcontain minimum25pages (excludingFrontand
Backmatter).
2 Thegroupsize forthefinalreport presentationshouldnot belessthan5studentsor exceed7students.
3 Therewill beanend –semesterpresentationbasedonthebookreport.
Assessment :
Term Work :
1 Termworkshallconsistofminimum8experiments.
2 Thedistributionofmarksfortermworkshallbeasfollows:Assig
nment : 10Marks
Attendance :5Marks
Presentationslides : 5
Marks BookReport(hardcopy) : 5Marks
3 Thefinalcertificationandacceptance oftermwork ensuresthesatisfactoryperformance
oflaboratoryworkand minimumpassingin the termwork.
Internal oral: Oral ExaminationwillbebasedonaGD&theProject/BookReportpresentation.
GroupDiscussion:10marksProje
ct Presentation : 10
MarksGroupDynamics: 5Marks
Books Recommended:TextbooksandReferencebooks
1 Arms,V.M.(2005). Humanitiesfortheengineeringcurriculum:Withselected
chaptersfromOlsen/Huckin:Technicalwritingandprofessionalcommunication,sec
ondedition . Boston,MA: McGraw -Hill.
2 Bovée,C.
L.,&Thill,J.V.(2021). Businesscommunicationtoday .UpperSaddleRiver,NJ:Pears
on.
3 Butterfield,J.(2017). Verbal
communication:Softskillsforadigitalworkplace .Boston,MA: CengageLearning.
4 Masters, L. A., Wallace, H. R., & Harwood, L. (2011). Personal development for
lifeandwork . Mason: South -WesternCengageLearning.
5 Robbins, S. P., Judge, T. A., & Campbell, T. T. (2017). Organizational
behaviour .Harlow,England: Pearson.
6 MeenakshiRaman,SangeetaSharma(2004)TechnicalCommunication,PrinciplesandPrac
tice.Oxford UniversityPress
7 ArchanaRam(2018)PlaceMentor,Tests
ofAptitudeforPlacementReadiness.OxfordUniversityPress
8 SanjayKumar
&PushpLata(2018).CommunicationSkillsaworkbook,NewDelhi:OxfordUniversityP
ress.



Course C ode Course Name Credits
CSM501 Mini Project 2A 02

Page 42

Objectives
1 To understand and identify the problem
2 To apply basic engineering fundamentals and attempt to find solutions to the problems.
3 Identify, analyze, formulate and handle programming projects with a comprehensive and
systematic approach
4 To develop communication skills and improve teamwork amongst group members and
inculcate the process of self -learning and research.
Outcome: Learner will be able to…
1 Identify societal/research/innovation/entrepreneurship problems through appropriate
literature surveys
2 Identify Methodology for solving above problem and apply engineering knowledge and
skills to solve it
3 Validate, Verify the results using test cases/benchmark data/theoretical/
inferences/experiments/simulations
4 Analyze and evaluate the impact of solution/product/research/innovation
/entrepreneurship towards societal/environmental/sustainable development
5 Use standard norms of engineering practices and project management principles during
project work
6 Communicate through technical report writing and oral presentation.
● The work may result in research/white paper/ article/blog writing and publication
● The work may result in business plan for entrepreneurship product created
● The work may result in patent f iling.
7 Gain technical competency towards participation in Competitions, Hackathons, etc.
8 Demonstrate capabilities of self -learning, leading to lifelong learning.
9 Develop interpersonal skills to work as a member of a group or as leader
Guidelines for Mini Project
1 Mini project may be carried out in one or more form of following:
Product preparations, prototype development model, fabrication of set -ups, laboratory
experiment development, process modification/development, simulation, software
development, integration of software (frontend -backend) and hardware, statistical
data an alysis, creating awareness in society/environment etc.
2 Students shall form a group of 3 to 4 students, while forming a group shall not be
allowed less than three or more than four students, as it is a group activity.
3 Students should do survey and i dentify needs, which shall be converted into problem
statement for mini project in consultati on with faculty supervisor or
head of department/internal committee of faculties.
4 Students shall submit an implementation plan in the form of Gantt/PERT/CPM chart,
which will cover weekly activity of mini projects.
5 A logbook may be prepared by each group, wherein the group can record weekly work
progress, guide/supervisor can verify and record notes/comments.
6 Faculty supervisors may give inputs to students during mini project activity; however,
focus shall be on self -learning.
7 Students under the guidance of faculty supervisor shall convert the best solution into a
working model using various components of their domain areas and demonstrate.
8 The solution to be validated with proper justification and report to be compiled in
standard format of University of Mumbai. Software requirement specification (SRS)
documents, research papers, competition certificates may be submitted as part of
annexure to the report.

Page 43

9 With the focus on self -learning, innovation, addressing societal/research/innovation
problems and entrepreneurship quality development within the students through the
Mini Projects, it is preferable that a single project of appropriate level and quality be
carried out in two semesters by all the groups of the students. i.e. Mini Project 2 in
semesters V and VI.
10 However, based on the individual students or group capability, with the mentor‘s
recommendations, if the proposed Mini Project adhering to the qualitative aspects
mentioned above, gets completed in odd semester, then that group can be allowed to
work on the extension of the Mini Project with suitable improvements/modifications or
a completely new project idea in even seme ster. This policy can be adopted on a case
by case basis.

Term Work
The review/ progress monitoring committee shall be constituted by the heads of departments of
each institute. The progress of the mini project to be evaluated on a continuous basis, based on
the SRS document submitted. minimum two reviews in each semester.
In continuous assessment focus shall also be on each individual student, assessment based on
individual‘s contribution in group activity, their understanding and response to questi ons.
Distribution of Term work marks for both semesters shall be as below: Marks 25
1 Marks awarded by guide/supervisor based on logbook 10
2 Marks awarded by review committee 10
3 Quality of Project report 05
Review / progress monitoring committee may consider following points for assessment
based on either one year or half year project asmentioned in general guidelines
One-year project:
1 In one-year project (sem V and VI), first semester the entire theoretical solution shall be
made ready, including components/system selection and cost analysis. Two reviews will
be conducted based on a presentation given by a student group.
 First shall be for finalization of problem
 Second shall be on finalization of proposed solution of problem.
2 In the second semester expected work shall be procurement of component‘s/systems,
building of working prototype, testing and validation of results based on work completed
in an earlier semester.
 First review is based on readiness of building working prototype to be conducted.
 Second review shall be based on poster presentation cum demonstration of
working model in the last month of the said semester.
Half -year project:
1 In this case in one semester students‘ group shall complete project in all aspects including,
 Identification of need/problem
 Proposed final solution
 Procurement of components/systems
 Building prototype and testing
2 Two reviews will be conducted for continuous assessment,
 First shall be for finalization of problem and proposed solution
 Second shall be for implementation and testing of solution.

Mini Project shall be assessed based on following points
1 Clarity of problem and quality of literature Survey for problem identification

Page 44

2 Requirement Gathering via SRS/ Feasibility Study
3 Completeness of methodology implemented
4 Design, Analysis and Further Plan
5 Novelty, Originality or Innovativeness of project
6 Societal / Research impact
7 Effective use of skill set : Standard engineering practices and Project management
standard
8 Contribution of an individual‘s as member or leader
9 Clarity in written and oral communication
10 Verification and validation of the solution/ Test Cases
11 Full functioning of working model as per stated requirements
12 Technical writing /competition/hackathon outcome being met

In one year project (sem V and VI), first semester evaluation may be based on first 10 criteria and
remaining may be used for se cond semester evaluation of performance of students in mini
projects.
In case of half year projects (completing in V sem) all criteria in generic may be considered for
evaluation of performance of students in mini projects.

Guidelines for Assessment of Mini Project Practical/Oral Examination:
1 Report should be prepared as per the guidelines issued by the University of Mumbai.
2 Mini Project shall be assessed through a presentation and demonstration of working model
by the student project group to a panel of Internal and External Examiners preferably from
industry or research organizations having experience of more than five years approved by
the head of Institution.
3 Students shall be motivated to publish a paper/participate in competition based o n the work
in Conferences/students competitions.

Page 45

Course Code Course Name Credit
CSC601 DataAnalytics andVisualization 03


Pre-requisite:

Course Objectives: The course aims:
1 Understand the science of statistics and the scope of its potential applications.
2 Verifytheunderlyingassumptionsofaparticularanalysis.
3 Construct testable hypotheses that can be evaluated using common statistical analyses.
4 Conduct, present, and interpret common statistical analyses using any tool.
5 Summarize and present data in meaningful ways through visualization techniques.

Course Outcomes:
Aftersuccessful completion of the course students will be able to:
1 Apply qualitative and quantitative techniques to understand the data
2 Formulate testable hypotheses andevaluate them using common statistical analyses.
3 Perform regression analysis on a given data set for prediction and forecasting.
4 ApplyANOVAmethodtofindthestatistical differences between the means in a given data.
5 Fit anARIMA model for prediction and forecasting of time series data
6 Translatethedataintovisualcontextto identifypatterns,trendsandoutliersinlarge datasets.


Module Detailed Content Hours
1 IntroductiontotheScienceof Statistics. 5
1.1 Fundamental Elements of Statistics, Qualitative and
QuantitativeDataSummaries,Normaldistribution∙Sampling,TheCentralLi
mit
Theorem.
2 ConfidenceIntervalsandHypothesisTests. 6
2.1 StatisticalInference,Stating Hypotheses,TestStatisticsandp -
Values,EvaluatingHypotheses.
2.2 SignificanceTestsandConfidenceIntervals,InferenceaboutaPopulationMean,T
wo-SampleProblems.
3 Understanding the association between two continuous orquantitative
factors. 5
3.1 Simple Linear Regression, F-test and t-test for Simple Linear
Regression.
3.2 Multiplelinearregression,F -testandt -testforMultipleLinear
Regression.
4 Analysis ofVariance(ANOVA)andAnalysisforProportions. 12
4.1 One-WayandTwo -Wayanalysis ofVarianceandCovariance,F -testfor
ANOVA,Type IandTypeIIErrors.

Page 46

4.2 Analysis for proportions: One -Sample Tests for Proportions,Significance
Tests for a Proportion, Confidence Intervals for aProportion,Two -
SampleTestsforProportions,ConfidenceIntervalsfor

Page 47

DifferencesinProportions,SignificanceTestsforDifferencesin
Proportions.
5 Time SeriesAnalysis 6
5.1 OperationsonTimeSeriesanalysis,TestingaTimeSeriesfor
Autocorrelation, Plotting the Partial Autocorrelation Function, Fitting
anARIMAModel,RunningDiagnosticsonanARIMAModel
6 DataVisualization 5
6.1 Bargraphs,Linegraphs,Histogram,Boxplots,Scatterplots,andChoropleth(
map)plots,RadialBarplots
6.2 Timeseriesplots,CreatingDashboardusinganytool.
Total 39

Textbooks:
1 Teetor,P.(2011).Rcookbook.Sebastopol,CA:O'Reilly.ISBN9780596809157.
2 Chang,W. (2013).Rgraphicscookbook.Sebastopol,CA:O'Reilly.ISBN
9781449316952.

References:
1 AndyField,JeremyMilesandZoeField.(2012)DiscoveringStatisticsUsingR.
Publisher:SAGEPublicationsLtd.ISBN -13:978 -1446200469.
2 GarethJames,Daniela Witten,Trevor Hastieand RobertTibshirani. (2013)An
Introduction toStatisticalLearningwithApplicationsinR. Springer.
3 Han,Kamber,"DataMiningConceptsandTechniques",MorganKaufmann3ndEdition

Assessment:

InternalAssessment:
Assessmentconsistsoftwoclasstestsof20markseach.The first-
classtestistobeconductedwhenapprox.40%syllabusiscompletedandsecondclasstestwhenadditional40%s
yllabusis
completed.Durationofeachtestshallbeonehour.

End SemesterTheory 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.



Useful Links
1 https://onlinecourses.nptel.ac.in/noc21_cs45/preview
2 https://nptel.ac.in/courses/106107220

Page 48

Course Code Course Name Credit
CSC602 Cryptographyand SystemSecurity 03


Pre-requisite:BasicconceptsofOSILayer

Course Objectives: The course aims:
1 The concepts of classical encryption techniques and concepts of finite fields and number
theory.
2 Toexploretheworkingprinciplesandutilitiesofvariouscryptographicalgorithmsincluding
secretkeycryptography,hashesandmessagedigests,andpublickey algorithms
3 Toexplorethedesignissuesandworkingprinciplesofvariousauthenticationprotocols,PKI
standards.
4 ToexplorevarioussecurecommunicationstandardsincludingKerberos,IPsec,andSSL/TLS
and email.
5 The ability to use existing cryptographic utilities to build programs for secure communication.
6 The concepts of cryptographic utilities and authentication mechanisms to design secure
applications

Course Outcomes:
1 Identify information security goals, classical encryption techniques and acquire fundamental
knowledgeontheconceptsoffinitefieldsandnumbertheory.
2 Understand,compare andapply differentencryption anddecryption techniques tosolve
problems related to confidentiality and authentication
3 Applythe knowledgeof cryptographicchecksums and evaluatethe performanceof different
message digest algorithms for verifying the integrity of varying message sizes
4 Applydifferent digitalsignature algorithmsto achieveauthentication and createsecure
applications .
5 Applynetwork securitybasics, analyze different attacks onnetworks andevaluate the
performance of firewalls and security protocols like SSL, IPSec, and PGP
6 Apply the knowledge of cryptographic utilities and authentication mechanisms to design
secure applications


Module DetailedContent Hours
1 Introduction &NumberTheory
1.1 Services, Mechanisms and attacks -the OSI security architecture -
Networksecurity model -Classical Encryption techniques (Symmetric
cipher model,mono -alphabeticandpoly -
alphabeticsubstitutiontechniques:Vignere
cipher, playfaircipher,Hillcipher,transpositiontechniques:keyedand
keyless transposition ciphers, steganography). 7
2 BlockCiphers&PublicKeyCryptography 7
2.1 DataEncryptionStandard -Blockcipherprinciples -
blockciphermodesofoperationAdvancedEncryptionStandard (AES) -
TripleDES -Blowfish -
RC5algorithm.Publickeycryptography:Principlesofpublickeycryptosystems -
TheRSAalgorithm,Theknapsackalgorithm,El -GamalAlgorithm.Key
management – Diffie Hellman Keyexchange

Page 49

InternalAssessment: Assessment: 3 CryptographicHashes,MessageDigestsand DigitalCertificates 7
3.1 Authentication requirement – Authentication function , Types
ofAuthentication,MAC –Hashfunction –
SecurityofhashfunctionandMAC
–MD5 – SHA – HMAC – CMAC, Digital Certificate: X.509, PKI
4 Digitalsignatureschemesandauthentication Protocols 6
4.1 Digitalsignatureandauthenticationprotocols:NeedhamSchroederAuthentication
protocol,DigitalSignature Schemes – RSA, EI Gamal andSchnorr,DSS.
5 SystemSecurity 6
Operating System Security: Memory and Address Protection, File
Protection Mechanism,UserAuthentication.LinuxandWindows:Vulnerabilities,
FileSystem Security
Database Security: Database Security Requirements, Reliability and
Integrity,Sensitive Data, InferenceAttacks, Multilevel Database Security
6 Websecurity 6
6.1 Web Security Considerations,UserAuthenticationandSession
Management, Cookies, SSL, HTTPS, SSH, Web Browser Attacks,
Web Bugs, Clickjacking, CrossSite Request Forgery, Session
Hijacking andManagement, Phishing Technique, DNS Attack,
Secure
ElectronicTransaction, EmailAttacks,Firewalls,PenetrationTesting


Textbooks:
1 ComputerSecurityPrinciplesandPractice,WilliamStallings,SixthEdition,Pearson
Education
2 SecurityinComputing,CharlesP.Pfleeger,FifthEdition,PearsonEducation
3 NetworkSecurityandCryptography, BernardMenezes,CengageLearning
4 NetworkSecurityBible,EricCole,SecondEdition,Wiley
5 MarkStamp‘sInformationSecurityPrinciplesandPractice,Wiley

References:
1 WebApplication HackersHandbookbyWiley.
2 ComputerSecurity,DieterGollman,ThirdEdition,Wiley
3 CCNASecurityStudyGuide, Tim Boyle,Wiley
4 IntroductiontoComputerSecurity,MattBishop,Pearson.5.
5 CloudSecurityandPrivacy,TimMather,SubraKumaraswamy,ShahedLatif,O‘Riely
6 Cryptographyand Network Security,AtulKahate,TataMcGrawHill

Page 50

Assessmentconsistsoftwoclasstestsof20markseach.Thefirst -
classtestistobeconductedwhenapprox.40%syllabusiscompletedand second class test when
additional40% syllabus is completed.
Durationofeachtestshallbeonehour.

End SemesterTheory 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.



Useful Links
1 https://nptel.ac.in/courses/106105031
2 https://onlinecourses.nptel.ac.in/noc22_cs03/preview
3 https://www.coursera.org/learn/basic -cryptography -and-crypto -api

Page 51

Course Code Course Name Credit
CSC603 SoftwareEngineeringand Project Management 03


Pre-requisite:None

Course Objectives: The course aims:
1 Toprovidetheknowledgeofsoftwareengineeringdiscipline.
2 TounderstandRequirementsandanalyzeit
3 Todoplanningandapplyscheduling
4 Toapplyanalysis, anddevelopsoftwaresolutions
5 Todemonstrateandevaluaterealtimeprojectswithrespecttosoftwareengineeringprinciples
andApply testing and assure quality in software solution.
6 Tounderstandneedofprojectmanagementandprojectmanagementlifecycle.

Course Outcomes:
1 Understand and use basic knowledge in software engineering.
2 Identify requirements, analyze and prepare models.
3 Plan, schedule and track the progress of the projects.
4 Design & develop the software solutions for the growth of society
5 Apply testing and assure quality in software solutions
6 Generate project schedule and can construct, design and develop network diagram for
differenttypeofProjects.Theycanalsoorganizedifferentactivitiesofproject


Module DetailedContent Hours
1 IntroductiontoSoftwareEngineering
Nature of Software, Software Engineering, Software Process,
CapabilityMaturity Model (CMM) Generic Process Model, Prescriptive
ProcessModels: The Waterfall Model, V -model, Incremental Process
Models, Evolutionary ProcessModels,
ConcurrentModels,Agileprocess,Agility
Principles, Extreme Programming (XP), Scrum, Kanban model 08
2 RequirementsAnalysis and Cost Estimation 06
2.1 Software Requirements: Functional & non-functional – user-
systemrequirementengineering process –feasibilitystudies – elicitation –
validation&management –softwareprototyping –S/Wdocumentation –
Analysisandmodelling Requirement Elicitation, Software requirement
specification
(SRS)3Ps(people,productandprocess)ProcessandProjectmetricsSoftwareProjec
tEstimation:LOC,FP,EmpiricalEstimationModels -COCOMOII
Model
3 DesignEngineering 07

Page 52

3.1 Design Process & quality, Design Concepts, The design Model, Pattern -
basedSoftware Design. 4.2 Architectural Design :Design Decisions, Views,
Patterns, ApplicationArchitectures,ModelingComponentlevelDesign:
component,Designing class based components, conducting component -level
design, UserInterfaceDesign:Thegolden rules, Interface Design steps &
Analysis, Design
Evaluation
4 SoftwareRisk,Configuration Management 05
4.1 Risk Identification, Risk Assessment, Risk Projection, RMMM Software
Configuration management, SCM repositories, SCM process Software Quality
Assurance Task and Plan, Metrics, Software Reliability, Formal Technical
Review (FTR), Walkthrough.
5 Software Testing and Maintenance 05
5.1 Testing: Software Quality, Testing: Strategic Approach, Strategic Issues -
Testing: Strategies for Conventional Software, Object oriented software, Web
AppsValidating Testing - System Testing - Art of Debugging.
Maintenance : Software Maintenance -Software Supportability -
Reengineering - Business Process Reengineering - Software Reengineering -
Reverse Engineering - Restructuring - Forward Engineering.
6 IT Project Management and Project Scheduling 08
6.1 Introduction, 4 P‘s, W5HH Principle, Need for Project Management, Project
Life cycle and ITPM, Project Feasibility, RFP, PMBOK Knowledge areas,
Business Case, Project Planning, Project Charter and Project Scope.
6.2 Project Scheduling:Defining a Task Set for the Software Project, Timeline
chartsWBS, Developing the Project Schedule, Network Diagrams (AON,
AOA), CPM and PERT, Gantt Chart , Tracking the Schedule, Earned Value
Analysis



Textbooks:
1 Roger S. Pressman, Software Engineering:A practitioner's approach, McGraw Hill
2 Rajib Mall, Fundamentals of Software Engineering, Prentice Hall India
3 JohnM.Nicholas,ProjectManagementforBusinessandTechnology,3rdedition,Pearson
Education.

References:
1 ―SoftwareEngineering:APreciseApproach‖ Pankaj Jalote,WileyIndia
2 Ian Sommerville ― Software Engineering‖ 9th edition Pearson Education SBN -13: 978 -0- 13-
703515 -1, ISBN -10: 0 -13-703515 -2
3 PankajJalote,An integrated approach to Software Engineering, Springer/Narosa.

Page 53

Assessment:

InternalAssessment:
Assessmentconsistsoftwoclasstestsof20markseach.Thefirst -classtestistobeconductedwhen
approx.40%syllabusiscompletedandsecondclasstestwhenadditional40%syllabusiscompleted.Duratio
nof each test shall be one hour.

End SemesterTheory 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.


Useful Links
1 https://onlinecourses.swayam2.ac.in/cec21_cs21/preview
2
https://nptel.ac.in/courses/106101061
3
http://www.nptelvideos.com/video.php?id=911&c=9 4

Page 54

Course Code Course Name Credit
CSC604 MachineLearning 03


Pre-requisite: Data Structures, Basic Probability and Statistics, Algorithms

Course Objectives: The course aims:
1 TointroduceMachinelearningconcepts
2 TodevelopmathematicalconceptsrequiredforMachinelearningalgorithms
3 TounderstandvariousRegressiontechniques
4 TounderstandClusteringtechniques
5 TodevelopNeuralNetworkbasedlearningmodels

Course Outcomes:
Aftersuccessful completion of the course students will be able to:
1 Comprehend basics of Machine Learning
2 Build Mathematical foundation for machine learning
3 Understand various Machine learning models
4 Select suitable Machine learning models for a given problem
5 Build Neural Network based models
6 Apply Dimensionality Reduction techniques


Modul
e Detailed Content Hours
1 IntroductiontoMachine Learning 6
1.1 Introduction to Machine Learning, Issues in Machine Learning,
ApplicationofMachineLearning,StepsofdevelopingaMachineLearningAppl
ication.
SupervisedandUnsupervisedLearning:ConceptsofClassification,Clustering
andprediction,Training, Testingandvalidationdataset,cross
validation, overfitting and underfitting of model
PerformanceMeasures:MeasuringQualityofmodel -ConfusionMatrix,
Accuracy,Recall,Precision,Specificity,F1Score,RMSE
2 Mathematical Foundation forML 5
2.1 Systemof Linearequations,Norms,Innerproducts,LengthofVector,Distancebetw
een vectors, Orthogonal vectors
2.2 SymmetricPositiveDefiniteMatrices,Determinant,Trace,Eigenvaluesandvect
ors, Orthogonal Projections, Diagonalization, SVD and its applications
3 Linear Models 7
3.1 Theleast -
squaresmethod,MultivariateLinearRegression,RegularizedRegression, Using
Least -Squares Regression for classification
3.2 SupportVectorMachines
4 Clustering 4
4.1 Hebbian Learning rule

Page 55

4.2 Expectation -Maximization algorithm for clustering
5 Classification models 10
5.1 Introduction, Fundamental concept, Evolution of Neural Networks,
Biological Neuron, Artificial Neural Networks, NN architecture, McCulloch -
Pitts Model.Designing a simple network, Non-separable patterns, Perceptron
model with Bias. Activation functions, Binary, Bipolar, continuous, Ramp.
Limitations ofPerceptron.
5.2 PerceptronLearningRule.DeltaLearningRule(LMS -WidrowHoff),
Multi -
layerperceptronnetwork.Adjustingweightsofhidden layers.Errorbackpropagation
algorithm.
5.3 Logistic regression
6 Dimensionality Reduction 07
6.1 CurseofDimensionality.
6.2 Feature Selection and Feature Extraction
6.3 Dimensionality ReductionTechniques,Principal ComponentAnalysis.



Textbooks:
1 Nathalie Japkowicz & Mohak Shah, ― Evaluating Learning Algorithms:A
Classification Perspective‖, Cambridge.
2 Marc Peter Deisenroth,Aldo Faisal, Cheng Soon Ong, ―Mathematics for machine learning‖,
3 SamirRoyandChakraborty,―Introductiontosoft computing‖,PearsonEdition.
4 EthemAlpaydın, ―Introduction to Machine Learning‖, MITPress McGraw -Hill Higher
Education
5 Peter Flach, ―Machine Learning‖, Cambridge University Press

References:
1 TomM.Mitchell,―MachineLearning‖,McGrawHill
2 Kevin P. Murphy, ―Machine Learning ―AProbabilisticPerspective‖,MITPress
3 Stephen Marsland, ―Machine Learning anAlgorithmicPerspective‖, CRC Press
4 Shai Shalev -Shwartz, Shai Ben -David, ―Understanding Machine Learning‖, Cambridge
University Press
5 Peter Harrington, ―Machine Learning inAction‖, DreamTech Press

Assessment:

InternalAssessment:
Assessmentconsistsoftwoclasstestsof20markseach.Thefirst -classtestistobeconducted
whenapprox.40%syllabusiscompletedandsecondclasstestwhenadditional40%syllabusis completed.Dura
tion of each test shall beone hour.

End SemesterTheory 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 56

Useful links:






















































1 NPTEL
2 AI and MLCertification - Enroll in PGPAI MLCourses with Purdue (si mplilearn.com)
3 https://www.learndatasci.com/out/coursera -machine -learning/
4 https://www.learndatasci.com/out/google -machine -learning -crash -course/

Page 57

CourseCode Course Name Credit
CSDLO6011 High Performance Computing 03


CourseObjectives: Studentswilltryto:

1. Learntheconceptsofhigh -performancecomputing.
2. Gainknowledgeofplatformsforhighperformancecomputing.
3. Designandimplementalgorithmsforparallelprogrammingapplications.
4. AnalyzetheperformancemetricsofHighPerformanceComputing.
5. Understandtheparallelprogramming paradigm,algorithmsandapplications.
6. DemonstratetheunderstandingofdifferentHighPerformanceComputingtools.

CourseOutcomes: Studentswillbeableto:

1. UnderstandthefundamentalsofparallelComputing.
2. Describedifferentparallelprocessingplatformsinvolvedinachieving HighPerformanceCom
puting.
3. DemonstratetheprinciplesofParallelAlgorithmsandtheirexecution.
4. EvaluatetheperformanceofHPCsystems.
5. ApplyHPCprogrammingparadigmtoparallelapplications
6. DiscussdifferentcurrentHPCPlatforms.

Prerequisite: ComputerOrganization,CProgramm ing,DatastructuresandAlgorithmAnalysis.


DETAILED SYLLABUS:

Sr.
No. Module DetailedCo
ntent Hours
0 Prerequisite ComputerOrganization,CProgramming,Datastructuresan
dAlgorithmAnalysis. 02
I Introduction Introduction to Parallel Computing: Motivating
Parallelism,Scope of Parallel Computing, Levels of
parallelism
(instruction,transaction,task,thread,memory,function),Mod
els(SIMD,
MIMD,SIMT,SPMD,DataflowModels,Demand -
drivenComputation).
Self-learning Topics: Parallel Architectures:
Interconnectionnetwork, ProcessorArray,Multiprocessor. 05

Page 58

II ParallelProgra
mmingPlatfor
ms ParallelProgrammingPlatforms: ImplicitParallelis
m:Dichotomy of Parallel Computing Platforms,
Physical
OrganizationofParallelPlatforms,CommunicationCostsi
nParallel Machines.
Self-learning Topics: Trends in Microprocessor &
Architectures,Limitations of Memory System Performance. 04
III Parallel
Algorithm
And
Concurrency Principles of Parallel Algorithm Design:
Preliminaries,Decomposition Techniques,
Characteristics of Tasks
andInteractions,MappingTechniquesforLoadBalanci
ng,
Basic Communication operations: Broadcast and
ReductionCommunication types.
Self-learningTopics: ParallelAlgorithmModels 09
IV Performance
Measures
forHPC PerformanceMeasures: Speedup,executiontime,efficienc
y,cost, scalability, Effect of granularity on
performance, Scalability of Parallel Systems, Amdahl‘s
Law, Gustavson‘s Law.
Self-learningTopics: PerformanceBottlenecks. 05
V ProgrammingP
aradigms
forHPC Programming Using the Message -Passing
Paradigm
:Principles ofMessagePassingProgramming,TheBuildi
ngBlocks: Send and Receive Operations, MPI: the
MessagePassingInterface,Topology andEmbedding.
ParallelAlgorithms andApplications : 09
One-DimensionalMatrix -VectorMultiplication,Graph
Algorithms,Sample Sort, Two-
DimensionalMatrixVectorMultiplication.
Self-learningTopics: IntroductiontoOpenMP.
VI GeneralP
urposeGr
aphics
Processing
Unit(GPGPU)
Architecturean
dProgramming OpenCLDeviceArchitectures, IntroductiontoOpenCLProgram
ming.
Self-learning Topics: Introduction to CUDA
architecture, andIntroduction to CUDAProgramming. 05

Page 59

TextBooks:

1. AnanthGrama,AnshulGupta,GeorgeKarypis,VipinKumar,―IntroductiontoParallelCompu
ting‖, Pearson Education, Second Edition, 2007.
2. Kai Hwang, Naresh Jotwani, ―Advanced Computer Architecture:
Parallelism, Scalability,Programmability‖,McGraw Hill,Second Edition, 2010.
3. EdwardKandrotandJasonSanders,―CUDAbyExample –
AnIntroductiontoGeneralPurposeGPUProgramming‖,Addison -WesleyProfessional
©,2010.
4. GeorgHager,GerhardWellein,―Introd uctiontoHighPerformanceComputingforScientistsa
ndEngineers",Chapman &Hall/ CRCComputationalScienceseries, 2011.
5. Benedict Gaster, Lee Howes, David Kaeli, Perhaad Mistry, Dana Schaa
,―HeterogeneousComputingwithOpenCL‖,2ndEdition,Elsevier,2012.

Reference Books :

1. MichaelJ.Quinn,―ParallelProgramminginCwithMPIandOpenMP‖,McGraw -
HillInternational Editions, Computer Science Series, 2008.
2. KaiHwang,ZhiweiXu,―ScalableParallelComputing:Technology,Architecture,Programmi
ng‖, McGraw Hill, 1998.
3. LaurenceT.Yang,MinyiGuo, ―High -
PerformanceComputing:ParadigmandInfrastructure‖Wiley, 2006.
4. FayezGebali,―AlgorithmsandParallelComputing‖,JohnWiley&Sons,Inc.,2011.

OnlineReferences:
Sr.No. WebsiteName
1. https://onlinecourses.nptel.ac.in/noc21_cs46/preview
2. https://onlinecourses.nptel.ac.in/noc22_cs21/preview

Page 60

Assessment:
InternalAssessment(IA)for20marks:
• IAwillconsistofTwoCompulsoryInternalAssessmentTests.Approxima
tely40% to 50% of syllabus content must be covered in First IA Test
andremaining 40% to 50% of syllabus content must be covered in
Second IATest.

EndSemesterExamination: Someguidelinesforsettingthequestionpapersareas:

● Weightageofeachmoduleinendsemesterexaminationisexpectedtobe/willbep
roportionaltonumberofrespectivelecturehoursmentionedint hesyllabus.
● Questionpaperformat
• QuestionPaperwillcompriseofatotalof sixquestionseachcarrying20
marks. Q.1 will be compulsory and should cover maximum
contents ofthesyllabus
• Remainingquestions willbe mixedinnature (part(a)andpart(b)ofeach
question must be from different modules. For example, if Q.2 has
part (a)from Module 3 then part (b) must be from any other Module
randomlyselectedfromallthemodules)
• Atotalof fourquestions needtobeanswered.

• Suggestion:Laboratoryworkbasedontheabovesyllabuscanbeincorporatedasa
mini projectinCSM601:Mini -Project.

Page 61

Course Code Course Name Credit
CSDLO6012 Distributed Computing 03


Pre-requisite:CProgramming

Course Objectives: The course aims:
1 Toprovidestudentswithcontemporaryknowledgeindistributedsystems
2 Toequip studentswithskillstoanalyzeanddesigndistributedapplications.
3 Toprovidemasterskillstomeasuretheperformanceofdistributedsynchronization
algorithms
4 Toequipstudentswithskillstoavailabilityofresources
5 Toprovidemasterskillstodistributedfilesystem

Course Outcomes:
1 Demonstrate knowledge of the basic elements and concepts related to distributed system
technologies.
2 Illustrate the middleware technologies that support distributed applications such as RPC, RMI
and Object based middleware.
3 Analyze the various techniques used for clock synchronization and mutual exclusion
4 Demonstrate the concepts of Resource and Process management and synchronization
algorithms
5 Demonstrate the concepts of Consistency and Replication Management
6 Apply the knowledge of Distributed File System to analyze various file systems like NFS,
AFSandthe experiencein buildinglarge -scale distributedapplications


Module DetailedContent Hours
1 IntroductiontoDistributedSystems
1.1 CharacterizationofDistributed Systems:Issues,Goals,andTypesof
distributed systems, Distributed System Models, Hardware
concepts,Software Concept. 06
1.2 Middleware:ModelsofMiddleware,Servicesofferedbymiddleware,ClientServer
model.
2 Communication 06
2.1 LayeredProtocols, Interprocesscommunication(IPC):MPI,RemoteProcedureCall
(RPC),RemoteObjectInvocation,RemoteMethodInvocation(RMI )
2.2 MessageOrientedCommunication,StreamOrientedCommunication,GroupCommunica
tion
3 Synchronization 09
3.1 Clock Synchronization, Physical Clock, Logical Clocks, Election
Algorithms,MutualExclusion,DistributedMutualExclusion -
ClassificationofMutualExclusionAlgorithm,RequirementsofMutualExclusion
Algorithms,
Performance measure.
3.2 Non Token based Algorithms: Lamport Algorithm, Ricart –Agrawala‘s
Algorithm, Maekawa‘sAlgorithm

Page 62

3.3 TokenBasedAlgorithms:Suzuki -
Kasami‘sBroadcastAlgorithms,Singhal‘sHeuristic Algorithm, Raymond‘s
Tree.based Algorithm, Comparative
PerformanceAnalysis.
4 ResourceandProcessManagement 06
4.1 DesirableFeaturesofglobalSchedulingalgorithm,Taskassignmentapproach,
Load balancing approach, load sharing approach
4.2 Introduction to process management, process migration,
Threads, Virtualization,Clients, Servers, CodeMigration
5 Consistency, Replication andFaultTolerance 06
5.1 Introductiontoreplicationandconsistency,Data -CentricandClient -Centric
ConsistencyModels,ReplicaManagement
5.2 FaultTolerance:Introduction,Processresilience,Reliableclient -serverandgroup
communication,Recovery
6 DistributedFileSystemsandNameServices 06
6.1 Introduction and features of DFS, File models, File Accessing models, File -Caching
Schemes,FileReplication,CaseStudy:DistributedFileSystems(DSF),NetworkFileSyste
m(NFS),AndrewFileSystem(AFS),HDFS

Textbooks:
1 AndrewS.TanenbaumandMaartenVanSteen,―DistributedSystems:PrinciplesandParadigms,
2nd edition, Pearson Education.
2 GeorgeCoulouris,JeanDollimore,TimKindberg,,"DistributedSystems:ConceptsandDesign",
4th Edition, Pearson Education, 2005.
References:
1 A.S.TanenbaumandM.V.Steen,"DistributedSystems:PrinciplesandParadigms",Second
Edition, Prentice Hall, 2006.
2 M. L. Liu,―Distributed Computing PrinciplesandApplications‖, PearsonAddisonWesley,2004.
3 Learn to Master Distributed Computing by ScriptDemics, StarEdu Solutions

Assessment:

InternalAssessment:
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. Durationof each test shall be one hour.

End SemesterTheory 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.



Useful Links
1 https://onlinecourses.nptel.ac.in/noc21_cs87/
2 https://nptel.ac.in/courses/106106168

* Suggestion:Laboratoryworkbasedontheabovesyllabuscanbe incorporatedasaminiprojectinCSM601
:Mini -Project.

Page 63

Course Code: CourseTitle Credit
CSDLO6013 ImageandVideoProcessing 3


Prerequisite: EngineeringMathematics,Algorithms
Course Objectives:
1 Tointroducestudentstothebasicconceptsofimage processing,fileformats.
2 Toacquireanin -depthunderstandingofimageenhancementtechnqiues.
3 Togainknowledgeofimagesegmentationandcompressiontechniques.
4 Toacquirefundamentalsofimagetransformtechniques.

Course Outcomes
1 Togainfundamentalknowledgeof Imageprocessing.
2 Toapplyimageenhancementtechniques.
3 Toapplyimagesegmentationandcompressiontechniques.
4 Togainanin -depthunderstandingofimagetransforms.
5 Togainfundamentalunderstandingofvideoprocessing.


Module Content Hrs
1 DigitalImage Fundamentals 04
1.1 IntroductiontoDigitalImage,DigitalImageProcessingSystem,Samplingand
Quantization,
1.2 RepresentationofDigitalImage,Connectivity,ImageFileFormats:BMP,TIF
F and JPEG.
2 ImageEnhancement inSpatial domain 08
2.1 IntroductiontoImage Enhancement:GrayLevelTransformations,ZeroMe
moryPoint Operations,
2.2 HistogramProcessing,.
2.3 NeighbourhoodProcessing,SpatialFiltering,SmoothingandSharpeningFil
ters
3 Image Segmentation 06
3.1 Segmentation based on Discontinuities (point, Line, Edge)
3.2 ImageEdgedetectionusingRobert,Sobel,Previttmasks,ImageEdgedetection
using Laplacian Mask.

Page 64

3.3 RegionOrientedSegmentation:RegiongrowingbypixelAggregation,Spl
itand Merge
4 ImageTransforms 09
4.1 IntroductiontoUnitaryTransforms
4.2 DiscreteFourierTransform(DFT), InverseDFT,PropertiesofDFT,FastFo
urierTransform(FFT),
4.3 DiscreteHadamardTransform(DHT),InverseDHT,FastHadamardTr
ansform(FHT),DiscreteCosineTransform(DCT),InverseDCT
5 ImageCompression 08
5.1 Introduction, Redundancy,FidelityCriteria
5.2 LosslessCompressionTechniques:RunlengthCoding,ArithmeticCo
ding, Huffman Coding
5.3 LossyCompressionTechniques:ImprovedGrayScaleQuantization,Ve
ctorQuantization
6 DigitalVideoProcessing 04
6.1 IntroductiontoDigital VideoProcessing,SampledVideo
6.2 CompositeandComponentVideo,Digitalvideoformatsandapp
lications
Total 39



Textbooks:
1 RafaelC.GonzalezandRichardE.Woods,‗DigitalImageProcessing‘,PearsonEducationAsia,
Third Edition, 2009
2 S.Jayaraman,E. EsakkirajanandT.Veerkumar,―DigitalImageProcessing‖TataMcGrawHill
Education Private Ltd, 2009
3 Anil K. Jain, ―Fundamentals and Digital Image Processing‖, Prentice Hall of India
PrivateLtd,Third Edition
4 S.Sridhar,―DigitalImageProcessing‖,Oxford UniversityPress,Second Edition,2012.
5. Alan C. Bovik,―The Essential GuideToVideoProcessing‖AcademicPress,
6 YaoWang,JornOstermann,Ya -
QinZang,―VideoProcessing andCommunications‖,Prentice Hall, Signal Processing
series.

Page 65

References Books
1. DavidA. Forsyth,JeanPonce,―Computer Vision:AModernApproach‖,
PearsonEducation,Limited,2011
2. Malay K. Pakhira, ―Digital Image Processing and Pattern Recognition‖, Prentice Hall
ofIndia Private Ltd,Third Edition
3 B.Chandra and
D.DuttaMajumder,―DigitalImage ProcessingandAnalysis‖,PrenticeHallofIndia Private Ltd,
2011
4 KhalidSayood,―IntroductiontoDataCompression‖,ThirdEdition,MorganKaufmanMKPublic
ation


Assessment:
InternalAssessment:
Assessment consists of two class tests of 20 marks each. The first class test is to be
conductedwhen approximately 40% syllabus is completed and the second class test when an
additional 40%syllabusis completed. Duration ofeach test shall be onehour.
End SemesterTheory Examination:
1 Question paper will comprise a total of six questions.
2 All questions carry equal marks.
3 Questionswillbemixedinnature(forexamplesupposedQ.2haspart(a)frommodule3then part (b)
will be from any module other than module 3).
4 Only Four questions need to be solved.
5 Inquestion,paper weightageofeachmodulewillbeproportionaltothenumberofrespective lecture
hours as mentioned in the syllabus.

Useful Links
1 https://swayam.gov.in
2 https://nptel.ac.in/courses
3 https://www.coursera.org

* Suggestion:Laboratoryworkbasedontheabovesyllabuscanbeincorporatedasa
miniprojectinCSM601:Mini -Project.

Page 66

Lab Code Lab Name Credit
CSL601 DataAnalytics andVisualizationLab 1


Prerequisite:BasicPython
Lab Objectives:
1 Toeffectivelyusegraphlibraries suchasmatplotlib/seaborn/excelplots.
2 Toperformexploratorydataanalysisonagivendataset
3 Tofitastatisticalmodel(Regression,ANOVA,ARIMA)on a given data set
4 Toapplysuitablevisualizationtechniquesforidentifyingpatterns,trendsandoutliersinlarge
data sets.
Lab Outcomes:
At the end of the course, students will be able to —-
1 Use graph libraries such as matplotlib/Seaborn/Excel plots.
2 Perform exploratory data analysis and prepare the data for fitting a model
3 Builda statistical model(Regression,ANOVA, ARIMA)onagivendataset
4 Apply suitable visualization techniques to get insights from a given data set

Suggested Experiments: Students are required to complete at least 08 experiments Preferably
using RProgrammingLanguage.

Sr.No. Name of the Experiment
1 Getting introduced to graph libraries such as matplotlib/Seaborn/Excel plots.
2 Data Exploration: Knowing the data.
3 Data preparation and Cleaning.
4 Visualizationofdata.
5 Correlation and Covariance.
6 HypothesisTesting.
7 Simple Linear Regression.
8 Multiple Linear Regression.
9 TimeSeries Analysis.
10 Creating a Dashboard.

Useful Links:
1 https://onlinecourses.nptel.ac.in/noc21_cs45/preview
2 https://www.coursera.org/specializations/data -science -python
3 https://public.tableau.com/en -us/s/resources

Useful Links:
1 EffectiveDataVisualizationTheRightChartfortheRightData,SECONDEDITION,Steph
anieD. H. Evergreen -Evergreen Data & Evaluation,LLC
2 YanchangZhao,―RandDataMining:ExamplesandCaseStudies‖,Elsevier,1stEdition,
2012.

Page 67

3 BetterDataVisualizationsAGuideforScholars, Researchers, andWonks,Jonathan
Schwabish, Columbia University Press



TermWork:
1 Termworkshouldconsistof08 experiments.
2 Journal must include at least 2 assignments based onTheory and Practicals
3 The final certification and acceptance of term work ensures satisfactory performance
oflaboratory work and minimum passing marks in term work.
4 Total 25Marks (Experiments:15 -marks,AttendanceTheory&Practical:05 -marks,
Assignments: 05 -marks)
Oral & Practical exam
Based on the entire syllabus

Page 68

Lab Code Lab Name Credit
CSL602 Cryptographic andsystemsecurityLab 1


Prerequisite:OperatingSystem,Basics ofJavaandPythonProgramming.
Lab Objectives:
1 Tobeabletoapplytheknowledgeofsymmetriccryptographytoimplementsimpleciphers
2 TobeabletoanalyzeandimplementpublickeyalgorithmslikeRSAandElGamal
3 Toanalyzeandevaluateperformanceofhashingalgorithms
4 Toexplorethedifferentnetworkreconnaissancetoolstogatherinformationaboutnetworks.
Lab Outcomes:
1 Apply the knowledge of symmetric cryptography to implement simple ciphers
2 Analyze and implement public key algorithms like RSAand El Gamal
3 Analyze and evaluate performance of hashing algorithms
4 Explorethe differentnetwork reconnaissancetools togather information aboutnetworks
5 Usetools like sniffers,port scanners andother related toolsfor analyzing packetsin a network.
6 Apply and set up firewalls and intrusion detection systems using open source technologies and
toexploreemailsecurity.


Suggested Experiments: Students are required to complete at least 10 experiments.
Star(*)markedexperimentsarecompulsory.
Sr.No. Name of the Experiment
1* DesignandImplementationofa productcipherusingSubstitutionandTransposition
ciphers.
2* Implementation and analysis of RSAcryptosystem and Digital signature scheme
using RSA/El Gamal.
3* ImplementationofDiffieHellman Keyexchangealgorithm
4 For varying message sizes, test integrity of message using MD -5, SHA -1, and analyse
the performance of the two protocols. Use cryptAPIs.
5* Exploring wireless security tools like Kismet, NetStumbler etc.
6* Study the use of network reconnaissance tools likeWHOIS, dig,traceroute, nslookup
to gather information about networks and domain registrars.
7 Studyofpacketsniffertoolswireshark,: -1.Observerperformanceinpromiscuousaswellas
non-promiscuousmode. 2. Showthe packets canbe traced basedon different
filters.
8* Downloadand installnmap. Use itwith different optionsto scan openports, perform
OS fingerprinting, do a ping scan, tcp port scan, udp port scan, etc. .
9* DetectARPspoofingusingnmapand/oropensourcetoolARPWATCH and wireshark
10 Use the NESSUS/ISO Kaali Linux tool to scan the network for vulnerabilities

Page 69

11 Set up IPSEC under LINUX. b) Set up Snort and study the logs. c) Explore the GPG
tooloflinuxtoimplementemailsecurity.

Useful Links:
1 www.leetcode.com
2 www.hackerrank.com
3 www.cs.usfca.edu/
4 www.codechef.com

TermWork:
1 Termworkshouldconsistof10experiments.
2 Journal must include at least 2 assignments.
3 The final certification and acceptance of term work ensures that satisfactory performance
oflaboratory work and minimum passing marks in term work.
4 Total 25Marks (Experiments:15 -marks,AttendanceTheory&Practical:05 -marks,
Assignments: 05 -marks)
Oral & Practical exam
Based on the entire syllabus of CSL602and CSC602

Page 70

Lab Code Lab Name Credit
CSL603 SoftwareEngineering andProjectManagement Lab 1


Prerequisite:KnowledgeofLinuxOperatingsystem,installationandconfigurationofservicesan
d command line basics,Basics of ComputerNetworksand Software
Development Life cycle.
Lab Objectives:
1 TounderstandDevOpspracticeswhichaimstosimplifySoftwareDevelopment LifeCycle.
2 TobeawareofdifferentVersionControltoolslikeGIT,CVSorMercurial
3 ToIntegrateanddeploytoolslikeJenkinsandMaven,whichisusedtobuild,testanddeploy
applications in DevOps environment
4 TounderstandtheimportanceofJenkinstoBuildanddeploySoftware Applicationsonserver
environment
5 TouseDockertoBuild,shipandmanageapplicationsusingcontainerization
6 TounderstandtheconceptofInfrastructureasacodeandinstallandconfigureAnsibletool
Lab Outcomes:
1 TounderstandthefundamentalsofDevOpsengineeringandbe fullyproficientwithDevOps
terminologies, concepts, benefits, and deployment options to meet your business requirements
2 Toobtaincompleteknowledgeofthe―versioncontrolsystem‖toeffectivelytrackchanges
augmented with Git and GitHub
3 Understand the importance of Selenium and Jenkins to test SoftwareApplications
4 TounderstandtheimportanceofJenkinstoBuildanddeploySoftwareApplicationsonserver
environment
5 Tounderstand concept
ofcontainerizationandAnalyzetheContainerizationofOSimagesanddeployment of applications
over Dockerk.
6 To Synthesize software configurationandprovisioning usingAnsible.


Suggested Experiments: Students are required to complete at least 10 experimentsfrom the list
givenbelow.
Star(*)markedexperimentsarecompulsory.
Sr.No. Name of the Experiment
1 TounderstandDevOps:Principles,Practices,andDevOpsEngineerRoleand
Responsibilities
2 TounderstandVersionControlSystem/SourceCodeManagement,installgitand
create a GitHub account
3 ToPerformvariousGIToperationsonlocalandRemote repositoriesusingGIT
Cheat -Sheet
4 TounderstandContinuousIntegration,installandconfigureJenkinswith

Page 71

Maven/Ant/Gradle to setup a build Job
5 ToBuildthepipelineofjobsusingMaven/Gradle/AntinJenkins,createapipeline
scripttoTestanddeployan applicationoverthetomcatserver.
6 To understand JenkinsMaster -SlaveArchitectureandscaleyourJenkinsstandalone
implementation by implementing slave nodes.
7 ToSetupandRunSeleniumTestsinJenkinsUsingMaven.
8 To understand DockerArchitecture andContainer LifeCycle, install Dockerand
execute docker commands to manage images and interact with containers
9 TolearnDockerfileinstructions,buildanimageforasamplewebapplicationusing
Dockerfile.
10 ToinstallandConfigurePullbasedSoftwareConfigurationManagementand
provisioning tools using Puppet
11 TolearnSoftwareConfigurationManagementandprovisioningusingPuppet
Blocks(Manifest, Modules, Classes, Function)
12 ToprovisionaLAMP/MEANStackusingPuppetManifest.

Useful Links:
1 https://nptel.ac.in/courses/128106012
2 https://www.edureka.co/devops -certification -training
3 https://www.coursera.org/professional -certificates/devops -and-software -engineering

TermWork:
1 Termworkshouldconsistof10experiments.
2 Journal must include at least 2 assignments.
3 The final certification and acceptance of term work ensures that satisfactory performance
oflaboratory work and minimum passing marks in term work.
4 Total 25Marks(Experiments:15 -marks,AttendanceTheory&Practical:05 -marks,
Assignments: 05 -marks)
Oral & Practical exam
Based on the entire syllabus of CSL603 and CSC603

Page 72

Lab Code Lab Name Credit
CSL604 Machine LearningLab 1


Prerequisite:CProgrammingLanguage.
Lab Objectives:
1 To introduce platforms suchas Anaconda,COLAB suitableto Machinelearning
2 ToimplementvariousRegressiontechniques
3 TodevelopNeuralNetworkbasedlearningmodels
4 ToimplementClusteringtechniques
Lab Outcomes:
Aftersuccessful completion of the course students will be able to:
1 Implement various Machine learning models
2 Apply suitable Machine learning models for a given problem
3 Implement Neural Network based models
4 Apply Dimensionality Reduction techniques


Suggested Experiments: Students are required to complete at least 10 experiments.
Sr.No. Name of the Experiment
1 Introduction to platforms such asAnaconda, COLAB
2 StudyofMachineLearningLibrariesandtools(Pythonlibrary,tensorflow,keras,...)
Implementation of following algorithms fora given example data set -
3 Linear Regression.
4 Logistic Regression.
5 SupportVectorMachines
6 Hebbian Learning
7 Expectation -Maximization algorithm
8 McCulloch Pitts Model.
9 Single Layer Perceptron Learning algorithm
10 Error BackpropagationPerceptronTrainingAlgorithm
11 Principal ComponentAnalysis
12 Applicationsofabovealgorithmsasa casestudy(E.g.HandWriting Recognition
using MNISTdata set, classification using IRIS data set, etc)

Useful Links:
1 https://www.learndatasci.com/out/edx -columbia -machine -learning/
2 https://www.learndatasci.com/out/oreilly -hands -machine -learning -scikit -learn -keras -and-ten
sorflow -2nd-edition/
3 https://www.learndatasci.com/out/google -machine -learning -crash -course/

Page 73

4 https://www.learndatasci.com/out/edx -columbia -machine -learning/

TermWork:
1 Termwork shouldconsistof10experiments.
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 25Marks (Experiments:15 -marks,AttendanceTheory&Practical:05 -marks,
Assignments: 05 -marks)
Oral & Practical exam
Based on the entire syllabus of CSL604and CSC604

Page 74

Lab Code Lab Name Credit
CSL605 Skill Based Lab course :Cloud Computing 2


Prerequisite:ComputerNetworks
Lab Objectives:
1 Tomakestudentsfamiliarwithkeyconceptsofvirtualization.
2 Tomakestudentsfamiliarwithvariousdeploymentmodelsofcloudsuchasprivate,public,hybrid
and community so that they start using and adopting appropriate types of cloud fortheir
application.
3 TomakestudentsfamiliarwithvariousservicemodelssuchasIaaS,SaaS,PaaS,Securityasa
Service (SECaaS) and Database as a Service.
4 Tomakestudentsfamiliarwithsecurityandprivacyissuesincloudcomputingandhowtoaddress
them.
Lab Outcomes:
1 Implementdifferenttypesofvirtualization techniques.
2 Analyze various cloud computing service models and implement them to solve the
givenproblems.
3 Design and develop real world web applications and deploy them on commercial cloud(s).
4 Explain major security issues in the cloud and mechanisms to address them.
5 Explore various commercially available cloud services and recommend the appropriate
onefor the given application.
6 Implement the concept of containerization

Theory:


Module
Detailed Contents
Hou
rs
1 Introduction and overview of cloud computing. To understand the
originofcloudcomputing,cloudcubemodel, NIST model, characteristics
ofcloud,differentdeployment models service models,
advantages and disadvantages. 4

Page 75

2 ConceptofVirtualizationalongwiththeirtypes,structuresandmechanisms.De
monstrationofcreatingandrunningVirtualmachinesinsidehostedhypervisors
likeVirtualBoxandKVMwiththeircomparison based on various
virtualization parameters. 4

3 Functionalityof Bare -
metalhypervisorsandtheirrelevanceincloudcomputingplatforms.Installation
,configureandmanageBareMetalhypervisoralongwithinstructionstocreatea
ndrunvirtualmachinesinsideit.ItshouldalsoemphasizeonaccessingVMsindif
ferentenvironments along with additional se rvices provided by them like
Loadbalancing,Auto -Scaling, Security etc. 4

Lab: (Teachersarerequestedtocompleteabovetheorybeforestaringlabwork)

1 Title: TostudyandImplementInfrastructureasaServiceusingAWS/Microsof
tAzure.
Objective: To demonstrate the steps to create and run virtual
machinesinsidea Public cloud platform. This experiment should
emphasize oncreating and running Linux/Windows Virtual machines
inside AmazonEC2orMicrosoftAzure Compute and accessing them using
RDP orVNC tools. 4
2 Title: To study and Implement Platform as a Service using AWS
ElasticBeanstalk/ MicrosoftAzureApp Service.
Objective: To demonstrate the steps to deploy Web applications or
WebserviceswrittenindifferentlanguagesonAWSElasticBeanstalk/Micros
oftAzureApp Service. 4
3 Tostudy andImplementStorageasaServiceusingOwnCloud/AWSS3,
Glaciers/Azure Storage. 2
4 TostudyandImplementDatabaseasaServiceonSQL/NOSQLdatabaseslike
AWSRDS,AZURESQL/MongoDBLab/Firebase. 2
5 Title: To study and Implement Security as a Service on
AWS/Azure Objective: TounderstandtheSecuritypracticesavailableinpubli
ccloudplatformsandtodemonstratevariousThreatdetection,Dataprotectiona
ndInfrastructureprotection servicesinAWSandAzure. 3

Page 76

6 Title: To study and implement Identity and Access Management
(IAM) practicesonAWS/Azure cloud.
Objective: TounderstandtheworkingofIdentityandAccessManagement
IAM in cloud computing and to demonstrate the case studybased on
Identity and Access Management (IAM) on AWS/Azure cloudplatform. 2
7 Title: TostudyandImplement ContainerizationusingDocker
Objective: ToknowthebasicdifferencesbetweenVirtualmachineandContainer.
It involves demonstration of creating, finding, building, installing,and
running Linux/Windows application containers inside a local machine
orcloud platform. 4
8 Title: To study and implement container orchestration using
Kubernetes Objective: TounderstandthestepstodeployKubernetesClustero
nlocalsystems,deployapplicationsonKubernetes,creatingaServiceinKuber
netes,developKubernetesconfigurationfilesinYAMLand creatingadeploym
ent inKubernetesusingYAML, 2
9 Mini -project: Design a Web Application hosted on a public
cloudplatform [It should cover the concept of IaaS, PaaS, DBaaS,
Storage as aService, Security as a Service etc.] 4




Suggested Experiments: Students are required to complete the above experiments.
Sr.No. Assignment
1 Assignmentbasedonselectionofsuitablecloudplatformsolutionbasedonrequiremen
t analysis considering given problem statement
2 Assignment on recent trends in cloud computing and related technologies
3 Assignment on comparative study of
differentcomputingtechnologies[Parallel,
Distributed,Cluster, Grid,Quantum)
4
ComparativestudyofdifferenthostedandbaremetalHypervisorswithsuitableparam
eters along with their use in public/private cloud platform
5
AssignmentonexploreandcomparethesimilartypeofservicesprovidedbyAWSa
ndAzure [Anyten services]

Page 77

Useful Links:
1 https://docs.aws.amazon.com/
2 https://docs.microsoft.com/en -us/azure
3 https://kubernetes.io/docs/home/
4 https://docs.docker.com/get -started/

TermWork:
1 Termworkshouldconsistof10experimentsandminiproject.
2 Journal must include at least 3 assignments.
3 The final certification and acceptance of term work ensures satisfactory performance
oflaboratory work and minimum passing marks in term work.
4 Total 25Marks(Experiments:15 -marks,AttendanceTheory&Practical:05 -marks,
Assignments: 05 -marks)

Oral examination will be based on Laboratory work, mini project and above syllabus

Page 78

Incaseofhalfyearprojects(completinginVIsem)allcriteria‘singenericmaybeconsideredfor
evaluation of performance of students in mini projects. In one year project (sem V and VI), first semester evaluation may be based on first 10
criteriaand remaining may be used for second semester evaluation of performance of students
in miniprojects. 2 Requirement gathering via SRS/ Feasibility Study
3 Completeness of methodology implemented
4 Design,Analysis and Further Plan
5 Novelty,OriginalityorInnovativenessofproject
6 Societal / Research impact
7 Effectiveuseof skillset:StandardengineeringpracticesandProjectmanagementstanda
rd
8 Contributionofanindividual‘sasmemberorleader
9 Clarity in written and oral communication
10 Verificationandvalidationofthesolution/TestCases
11 Fullfunctioningofworkingmodelasperstated requirements
12 Technicalwriting/competition/hackathonoutcomebeingmet




Guidelines forAssessment ofMini ProjectPractical/OralExamination:
1 Report should be prepared as per the guidelines issued by the University of Mumbai.
2 MiniProjectshall be assessed through a presentation and demonstration of
workingmodelbythestudentprojectgrouptoapanelofInternalandExternalExaminersprefera
bly from industry or research organizations having experience of more than fiveyears
approved by the head of Institution.
3 Students shall be motivated to publish a paper/participate in competition based on
thework in Conferences/students competitions.

Page 79

Course code Course Name Credits
CSM601 Mini Project 2B 02

Objectives
1 To understand and identify the problem
2 To apply basic engineering fundamentals and attempt to find solutions to the problems.
3 Identify, analyze, formulate and handle programming projects with a comprehensive and
systematic approach
4 To develop communication skills and improve teamwork amongst group members and
inculcate the process of self -learning and research.
Outcome: Learner will be able to…
1 Identify societal/research/innovation/entrepreneurship problems through appropriate
literature surveys
2 Identify Methodology for solving above problem and apply engineering knowledge and
skills to solve it
3 Validate, Verify the results using test cases/benchmark data/theoretical/
inferences/experiments/simulations
4 Analyze and evaluate the impact of solution/product/research/innovation
/entrepreneurship towards societal/environmental/sustainable development
5 Use standard norms of engineering practices and project management principles during
project work
6 Communicate through technical report writing and oral presentation.
● The work may result in research/white paper/ article/blog writing and publication
● The work may result in business plan for entrepreneurship product created
● The work may result in patent filing.
7 Gain technical competency towards participation in Competitions, Hackathons, etc.
8 Demonstrate capabilities of self -learning, leading to lifelong learning.
9 Develop interpersonal skills to work as a member of a group or as leader
Guidelines for Mini Project
1 Mini project may be carried out in one or more form of following:
Product preparations, prototype development model, fabrication of set -ups, laboratory
experiment development, process modification/development, simulation, software
development, integration of software (frontend -backend) and hardware, statistical data
analysis, creating awareness in society/environment etc.
2 Students shall form a group of 3 to 4 students, while forming a group shall not be allowed
less than three or more than four students, as it is a group activity.
3 Students should do survey and identify needs, which shall be converted into problem
statement for mini project in consultation with faculty supervisor/head
of department/internal committee of faculties.
4 Students shall submit an implementation plan in the form of Gantt/PERT/CPM chart,
which will cover weekly activity of mini projects.
5 A logbook may be prepared by each group, wherein the group can record weekly work
progress, guide/supervisor can verify and record notes/comments.
6 Faculty supervisors may give inputs to students during mini project activity; however,
focus shall be on self -learning.
7 Students under the guidance of faculty supervisor shall convert the best solution into a
working model using various components of their domain areas and demonstrate.
8 The solution to be validated with proper justification and report to be compiled in
standard format of University of Mumbai. Software requirement specification (SRS)
documents, research papers, competition certificates may be submitted as part of annexure
to the report.

Page 80

9 With the focus on self -learning, innovation, addressing societal/research/innovation
problems and entrepreneurship quality development within the students through the Mini
Projects, it is preferable that a single project of appropriate level and quality be carried
out in two semesters by all the groups of the students. i.e. Mini Project 2 in semesters V
and VI.
10 However, based on the individual students or group capability, with the mentor‘s
recommendations, if the proposed Mini Project adhering to the qualitative aspects
mentioned above, gets completed in odd semester, then that group can be allowed to
work on the extension of the Mini Project with suitable improvements/modifications or a
completely new project idea in even seme ster. This policy can be adopted on a case by
case basis.
Term Work
The review/ progress monitoring committee shall be constituted by the heads of departments of
each institute. The progress of the mini project to be evaluated on a continuous basis, based on
the SRS document submitted. minimum two reviews in each semester.
In continuous assessment focus shall also be on each individual student, assessment based on
individual‘s contribution in group activity, their understanding and response to questions.
Distribution of Term work marks for both semesters shall be as below: Marks 25
1 Marks awarded by guide/supervisor based on logbook 10
2 Marks awarded by review committee 10
3 Quality of Project report 05
Review / progress monitoring committee may consider following points for assessment
based on either one year or half year project as mentioned in general guidelines
One-year project:
1 In the first semester the entire theoretical solution shall be made ready, including
components/system selection and cost analysis. Two reviews will be conducted based on
a presentation given by a student group.
 First shall be for finalization of problem
 Second shall be on finalization of proposed solution of problem.
2 In the second semester expected work shall be procurement of component‘s/systems,
building of working prototype, testing and validation of results based on work completed
in an earlier semester.
 First review is based on readiness of building working prototype to be co nducted.
 Second review shall be based on poster presentation cum demonstration of working
model in the last month of the said semester.
Half -year project:
1 In this case in one semester students‘ group shall complete project in all aspects including,
 Identification of need/problem
 Proposed final solution
 Procurement of components/systems
 Building prototype and testing
2 Two reviews will be conducted for continuous assessment,
 First shall be for finalization of problem and proposed solution
 Second shall be for implementation and testing of solution.
Mini Project shall be assessed based on following points
1 Clarity of problem and quality of literature Survey for problem identification
2 Requirement gathering via SRS/ Feasibility Study
3 Completeness of methodology implemented

Page 81

4 Design, Analysis and Further Plan
5 Novelty, Originality or Innovativeness of project
6 Societal / Research impact
7 Effective use of skill set : Standard engineering practices and Project management
standard
8 Contribution of an individual‘s as member or leader
9 Clarity in written and oral communication
10 Verification and validation of the solution/ Test Cases
11 Full functioning of working model as per stated requirements
12 Technical writing /competition/hackathon outcome being met

In one year project (sem V and VI), first semester evaluation may be based on first 10 criteria
and remaining may be used for second semester evaluation of performance of students in mini
projects.
In case of half year projects (completing in VI sem) all criteria‘s in generic may be considered
for evaluation of performance of students in mini projects.

Guidelines for Assessment of Mini Project Practical/Oral Examination:
1 Report should be prepared as per the guidelines issued by the University of Mumbai.
2 Mini Project shall be assessed through a presentation and demonstration of working
model by the student project group to a panel of Internal and External Examiners
preferably from industry or research organizations having experience of more than five
years approved by the head of Institution.
3 Students shall be motivated to publish a paper/participate in competition based on the
work in Conferences/students competitions.