UNIVERSITY OF MUMBAI Bachelor of Engineering Computer

AC:

Item No.

UNIVERSITY OF MUMBAI

Bachelor of Engineering

Computer Engineering

Second Year with Effect from AY 2020-21

Third Year with Effect from AY 2021-22

Final Year with Effect from AY 2022-23

(REV- 2019 ‘C’ Scheme) from Academic Year 2019 – 20

Under

FACULTY OF SCIENCE & TECHNOLOGY

(As per AICTE guidelines with effect from the academic year 2019–2020)

Sr. No.

Heading

Particulars

Title of the Course

Fourth Year Engineering

( Computer Engineering)

Eligibility for Admission

After Passing Second Year Engineering as per

the Ordinance 0.6243

Passing Marks

40%

Ordinances /

Regulations ( if any)

Ordinance 0.6243

No. of Years / Semesters

8 semesters

Level

P.G. / U.G./ Diploma / Certificate

(Strike out which is not applicable)

Pattern

Yearly / Semester

(Strike out which is not applicable )

Status

New/ Revised

(Strike out which is not applicable )

To be implemented from

Academic Year

With effect from Academic Year:2021-2022

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

AC:

Item No.

UNIVERSITY OFMUMBAI

Syllabus for Approval

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 curriculum development.

Faculty resolved that course objectives and course outcomes are to be clearly defined for each

course, so that all faculty members in affiliated institutes understand the depth and approach of



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 syllabus etc.

There was a concern that the earlier revised curriculum more focused on providing information and

knowledge across various domains of the said program, which led to heavily loading of students in

terms of direct contact hours. In this regard, faculty of science and 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. Subsequently this will be carried forward for Third Year and Final Year Engineering in the

academic years 2022-23, 2023-24, respectively.

Dr. S.K. Ukarande Dr Anuradha Muzumdar

Associate Dean Dean

Faculty of Science and Technology Faculty of Science and Technology

University of Mumbai University of Mumbai

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 par     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 Revi  

efforts were made to use online contents more appropriately as additional learning materials to

enhance learning of students.

In the current revision based on the recommendation of AICTE model curriculum overall 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.

   culties of all the institute are required to motivate and encourage

learners to use additional online resources available on platforms such as NPTEL/ Swayam.

Learners can be advised to take up online courses, on successful completion they are required to

submit certification for the same. This will definitely help learners to facilitate their enhanced

learning based on their interest.

Dr. S.K.Ukarande Dr Anuradha Muzumdar

Associate Dean Dean

Faculty of Science and Technology Faculty of Science and Technology

University of Mumbai University of Mumbai

Preface by Board of Studies in

Computer Engineering

Dear Students and Teachers, we, the members of Board of Studies Computer

Engineering, are very happy to present Third Year Computer Engineering syllabus

effective from the Academic Year 2021-22 (REV-

will find this syllabus interesting, challenging, fulfill certain needs and expectations.

Computer Engineering is one of the most sought-after courses amongst engineering

students. The syllabus needs revision in terms of preparing the student for the

professional scenario relevant and suitable to cater the needs of industry in present day

context. The syllabus focuses on providing a sound theoretical background as well as

good practical exposure to students in the relevant areas. It is intended to provide a

modern, industry-oriented education in Computer Engineering. It aims at producing

trained professionals who can successfully 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 syllabus is finalized through a brain storming session attended by Heads of

Departments or senior faculty from the Department of Computer Engineering of the

affiliated Institutes of the Mumbai University. The syllabus falls in line with the

objectives of affiliating University, AICTE, UGC, and various accreditation agencies

by keeping an eye on the technological 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 showcase 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. LeenaRaga : 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

Program Structure for Fourth Year Computer Engineering

UNIVERSITY OF MUMBAI (With Effect from 2022-2023)

Semester VII

Course

Code

Course Name

Teaching Scheme

(Contact Hours)

Credits Assigned

Theory

Pract.

Tut.

Theory

Pract.

Total

CSC701

Machine Learning

CSC702

Big Data Analytics

CSDC

701X

Department Level

Optional Course-3

CSDC

702X

Department Level

Optional Course-4

CSIO701

Institute Level Optional

Course-1

CSL701

Machine Learning Lab

CSL702

Big Data Analytics Lab

CSDL

701X

Department Level

Optional Course-3 Lab

CSDL

702X

Department Level

Optional Course-4 Lab

CSP701

Major Project 1

Total

Course

Code

Course Name

Examination Scheme

Theory

Term

Work

Pract.

& oral

Total

Internal

Assessment

End

Sem

Exam

Exam.

Duration

(in Hrs)

Test

Avg

CSC701

Machine Learning

100

CSC702

Big Data Analysis

100

CSDC

701X

Department Level

Optional Course-3

100

CSDC

702X

Department Level

Optional Course-4

100

CSIO701

Institute Level Optional

Course-1

100

CSL701

Machine Learning Lab

CSL702

Big Data Analytics Lab

CSDL

701X

Department Level

Optional Course-3 Lab

CSDL

702X

Department Level

Optional Course-4 Lab

CSP701

Major Project 1

Total

100

400

150

725

Program Structure for Fourth Year Computer Engineering

UNIVERSITY OF MUMBAI (With Effect from 2022-2023)

Semester VIII

Course

Code

Course Name

Teaching Scheme

(Contact Hours)

Credits Assigned

Theory

Pract.

Tut.

Theory

Pract.

Total

CSC801

Distributed Computing

CSDC

801X

Department Level Optional

Course -5

CSDC

802X

Department Level Optional

Course -6

CSIO801

Institute Level Optional

Course -2

CSL801

Distributed Computing Lab

CSDL

801X

Department Level Optional

Course -5 Lab

CSDL

802X

Department Level Optional

Course -6 Lab

CSP801

Major Project 2

Total

Course

Code

Course Name

Examination Scheme

Theory

Term

Work

Pract

oral

Total

Internal Assessment

End

Sem

Exam

Duration

(in Hrs)

Test

Avg

CSC801

Distributed Computing

100

CSDC

801X

Department Level Optional

Course -5

100

CSDC

802X

Department Level Optional

Course -6

100

CSIO801

Institute Level Optional

Course -2

100

CSL801

Distributed Computing Lab

CSDL

801X

Department Level Optional

Course -5 Lab

CSDL

802X

Department Level Optional

Course -6 Lab

CSP801

Major Project 2

100

150

Total

320

175

125

700

Major Project 1 and 2 :

 Students can form groups with minimum 2 (Two) and not more than 4 (Four)

 Faculty Load : In Semester VII  ½ hour per week per project group

In Semester VIII  1 hour per week per project group

Program Structure for Computer Engineering

UNIVERSITY OF MUMBAI (With Effect from 2022-2023)

Department and Institute Optional Courses and Labs

Semester

Department/

Institute Optional

Courses and Labs

Subject

VII

Department Optional

Course -3

CSDC7011: Machine Vision

CSDC7012: Cyber Security

CSDC7013: Natural Language Processing

Department Optional

Lab -3

CSDL7011: Machine Vision Lab

CSDL7012: Cyber Security Lab

CSDL7013: Natural Language Processing Lab

Department Optional

Course -4

CSDC7021 : Augmented and Virtual Reality

CSDC7022 : Block Chain

CSDC7023 : Information Retrieval

Department Optional

Lab -4

CSDL7021 : Augmented and Virtual Reality Lab

CSDL7022 : Block Chain Lab

CSDL7023 : Information Retrieval Lab

Institute level

Optional Courses-I

Program Structure for Computer Engineering

UNIVERSITY OF MUMBAI (With Effect from 2022-2023)

Department and Institute Optional Courses and Labs

Semester

Department/

Institute Optional

Courses and Labs

Subject

VIII

Department Optional

Course -5

CSDC8011 : Deep Learning

CSDC8012 : Digital Forensic

CSDC8013 : Applied Data Science

Department Optional

Lab -5

CSDL8011 : Deep Learning Lab

CSDL8012 : Digital Forensic Lab

CSDL8013 : Applied Data Science Lab

Department Optional

Course -6

CSDC8021 : Optimization in Machine Learning

CSDC8022: High Performance Computing

CSDC8023: Social Media Analytics

Department Optional

Lab -6

CSDL8021 : Optimization in Machine Learning Lab

CSDL8022: High Performance Computing Lab

CSDL8023: Social Media Analytics Lab

Institute level

Optional Courses-II

Course Code:

Course Title

Credit

CSC701

Machine Learning

Prerequisite: Engineering Mathematics, Data Structures, Algorithms

Course Objectives:

To introduce the basic concepts and techniques of Machine Learning.

To acquire in depth understanding of various supervised and unsupervised algorithms

To be able to apply various ensemble techniques for combining ML models.

To demonstrate dimensionality reduction techniques.

Course Outcomes:

To acquire fundamental knowledge of developing machine learning models.

To select, apply and evaluate an appropriate machine learning model for the given

application.

To demonstrate ensemble techniques to combine predictions from different models.

To demonstrate the dimensionality reduction techniques.

Module

Content

Hrs

Introduction to Machine Learning

1.1

Machine Learning, Types of Machine Learning, Issues in Machine

Learning, Application of Machine Learning, Steps in developing a

Machine Learning Application.

1.2

Training Error, Generalization error, Overfitting, Underfitting, Bias-

Variance trade-off.

Learning with Regression and Trees

2.1

Learning with Regression: Linear Regression, Multivariate Linear

Regression, Logistic Regression.

2.2

Learning with Trees: Decision Trees, Constructing Decision Trees using

Gini Index (Regression), Classification and Regression Trees (CART)

2.3

Performance Metrics: Confusion Matrix, [Kappa Statistics], Sensitivity,

Specificity, Precision, Recall, F-measure, ROC curve

Ensemble Learning

3.1

Understanding Ensembles, K-fold cross validation, Boosting, Stumping,

XGBoost

3.2

Bagging, Subagging, Random Forest, Comparison with Boosting,

Different ways to combine classifiers

Learning with Classification

4.1

Support Vector Machine

Constrained Optimization, Optimal decision boundary, Margins and

support vectors, SVM as constrained optimization problem, Quadratic

Programming, SVM for linear and nonlinear classification, Basics of

Kernel trick.

4.2

Support Vector Regression, Multiclass Classification

Learning with Clustering

5.1

Introduction to clustering with overview of distance metrics and major

clustering approaches.

5.2

Graph Based Clustering: Clustering with minimal spanning tree

Model based Clustering: Expectation Maximization Algorithm,

Density Based Clustering: DBSCAN

Dimensionality Reduction

6.1

Dimensionality Reduction Techniques, Principal Component Analysis,

Linear Discriminant Analysis, Singular Valued Decomposition.

Total

Textbooks:



Ethem 

Tom M. 



References:



Margaret. H. Advanced Topics, Pearson

Education





Richard  

Publications.

Assessment:

Internal Assessment:

Assessment consists of two class tests of 20 marks each. The first class test is to be conducted

when approximately 40% syllabus is completed and the second class test when an additional 40%

syllabus is completed. Duration of each test shall be one hour.

End Semester Theory Examination:

Question paper will comprise a total of six questions.

All question carries equal marks

Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3

then part (b) will be from any module other than module 3)

Only Four questions need to be solved.

In question paper weightage of each module will be proportional to number of respective

lecture hours as mentioned in the syllabus.

Useful Digital Links

Data sets for Machine Learning algorithms: https://www.kaggle.com/datasets

Machine Learning repository- https://archive.ics.uci.edu/ml/index.php

Machine Learning from Coursera

https://towardsdatascience.com/machine-learning/home

https://onlinecourses.nptel.ac.in/noc21_cs85/preview

Course Code

Course Name

Credit

CSC702

Big Data Analysis

Prerequisite: Database, Data mining.

Course Objectives: The course aims:

To provide an overview of the big data platforms, its use cases and Hadoop ecosystem.

To introduce programming skills to build simple solutions using big data technologies such as

MapReduce, Scripting for No SQL and R

To learn the fundamental techniques and principles in achieving big data analytics with

scalability and streaming capability.

To enable students to have skills that will help them to solve complex real-world problems for

decision support.

Course Outcomes:

Understand the building blocks of Big Data Analytics.

Apply fundamental enabling techniques like Hadoop and MapReduce in solving real world

problems.

Understand different NoSQL systems and how it handles big data.

Apply advanced techniques for emerging applications like stream analytics.

Achieve adequate perspectives of big data analytics in various applications like

recommender systems, social media applications, etc.

Apply statistical computing techniques and graphics for analyzing big data.

Module

Detailed Content

Hours

Introduction to Big Data and Hadoop

1.1

Introduction to Big Data - Big Data characteristics and Types of Big Data

1.2

Traditional vs. Big Data business approach

1.3

Case Study of Big Data Solutions

1.4

Concept of Hadoop, Core Hadoop Components; Hadoop Ecosystem

Hadoop HDFS and MapReduce

2.1

Distributed File Systems: Physical Organization of Compute Nodes, Large-

Scale File-System Organization.

2.2

MapReduce: The Map Tasks, Grouping by Key, The Reduce Tasks,

Combiners, Details of MapReduce Execution, Coping With Node Failures.

2.3

Algorithms Using MapReduce: Matrix-Vector Multiplication by MapReduce,

Relational-Algebra Operations, Computing Selections by MapReduce,

Computing Projections by MapReduce, Union ,Intersection,

and Difference by MapReduce

2.4

Hadoop Limitations

NoSQL

3.1

Introduction to NoSQL, NoSQL Business Drivers

3.2

NoSQL Data Architecture Patterns: Key-value stores, Graph stores, Column

family (Bigtable)stores, Document stores, Variations of NoSQL architectural

patterns, NoSQL Case Study

3.3

NoSQL solution for big data, Understanding the types of big data problems;

Analyzing big data with a shared-nothing architecture; Choosing distribution

models: master-slave versus peer-to-peer; NoSQL systems to handle big data

problems.

Mining Data Streams

4.1

The Stream Data Model: A Data-Stream-Management System, Examples of

Stream Sources, Stream Queries, Issues in Stream Processing.

4.2

Sampling Data techniques in a Stream

4.3

Filtering Streams: Bloom Filter with Analysis.

4.4

Counting Distinct Elements in a Stream,Count-

Distinct Problem, Flajolet-Martin Algorithm, Combining Estimates,

Space Requirements

4.5

Counting Ones in a Window: The Cost of Exact Counts,The

Datar-Gionis-Indyk-Motwani Algorithm, Query Answering in theDGIM

Algorithm, Decaying Windows.

Real-Time Big Data Models

5.1

A Model for Recommendation Systems, Content-Based Recommendations,

Collaborative Filtering

5.2

Case Study: Product Recommendation

5.3

Social Networks as Graphs, Clustering of Social-Network Graphs, Direct

Discovery of Communities in a social graph

Data Analytics with R

6.1

Exploring Basic features of R, Exploring RGUI, Exploring RStudio, Handling

Basic Expressions in R, Variables in R, Working with Vectors, Storing and

Calculating Values in R, Creating and using Objects, Interacting with users,

Handling data in R workspace, Executing Scripts, Creating Plots,

Accessing help and documentation in R

6.2

Reading datasets and Exporting data from R, Manipulating and Processing

Data in R, Using functions instead of script, built-in functions in R

6.3

Data Visualization: Types, Applications

Textbooks:

Cre Anand Rajaraman and Jef



, Cambridge

UniversityPress





, Manning Press, Dreamtech Press.





 A guide for managers and the

rest of us, Manning Press.





References:

Bill Franks , The Big Data Tidal Wave: Finding Opportunities In HugeData

StreamsWithAdvancedAnalytics



,Wiley





, Dreamtech Press

Value Creation for

Business Leaders and Practitioners



,Wiley India Private Limited, 2014.

Jiawei Han and Micheline KamberTechniques



, Morgan

Kaufmann Publishers, 3rd ed, 2010.



Handbook



, Springer, 2nd edition,2010.



Approaches in Analyzing Unstructured Data



, Cambridge University Press, 2006.

V



, MITPress, 2010.

Assessment:

Internal Assessment:

Assessment consists of two class tests of 20 marks each. The first-class test is to be conducted

when approx. 40% syllabus is completed and second class test when additional40% syllabus is

completed. Duration of each test shall be one hour.

End Semester Theory Examination:

Question paper will consist of 6 questions, each carrying 20 marks.

The students need to solve a total of 4 questions.

Question No.1 will be compulsory and based on the entire syllabus.

Remaining question (Q.2 to Q.6) will be selected from all the modules.

Useful Links

https://nptel.ac.in/courses/106104189

https://www.coursera.org/specializations/big-data#courses

https://www.digimat.in/nptel/courses/video/106106169/L01.html

https://www.coursera.org/learn/nosql-databases#syllabus

https://www.coursera.org/learn/basic-recommender-systems#syllabus

Course Code

Course Name

Credit

CSDC7011

Machine Vision

Pre-requisite: Computer Graphics

Course Objectives: The course aims:

To understand the need and significance Machine Vision

To explore basics of image processing

To explore the components of Machine Vision System

To develop application using machine Vision

To study transformation, interpolation, filters.

Course Outcomes: Learners will be able to

Elaborate the components of Machine Vision Application

Perform image ,video preprocessing operations

Explain various transformations, interpolation.

Elaborate motion tracking in video.

Analyze and Implement appropriate filtering techniques for a given problem.

Develop applications based on machine vision..

Module

Detailed Content

Hours

Introduction to Machine Vision

Computer and Human Vision Systems., The Human Eye,

Computer versus Human Vision Systems, Evolution of

Computer Vision, Computer/Machine Vision and Image

Processing, Applications of Computer Vision

Digital Image Fundamentals

Digital Image, Monochrome and Color Images, Image Brightness

and Contrast., 2D, 3D, and 4D Images, Digital Image

Representation , Digital Image File Formats, Fundamental Image

Operations, Points, Edges, and Vertices , Point Operations ,

Thresholding ,Brightness, Geometric Transformations , Spatial

Transformation , Affine Transformation, Image Interpolation

,Nearest-Neighbor Interpolation ,Bilinear Interpolation , Bi-cubic

Interpolation ,Fundamental Steps in Digital Image Processing.

Machine Vision and System Components

Machine Vision System, Machine Vision Camera: CCD and

CMOS Image Sensors, TDI Sensor, Camera Type - Area Scan

Cameras, Line Scan Cameras, Smart Cameras, Camera Lens-

Resolution, Contrast and Sharpness, Lenses and their parameters:

Types of Lenses, Lens Mounts, Lens Selection Examples-Field of

View Much larger than Camera sensor size or Smaller or close to

Camera Sensor size, Machine Vision Lighting: Lighting: Light

Sources in Machine Vision, Illumination Techniques-Backlighting,

Front Lighting, Diffused Lighting, Oblique Lighting, Dark Field

Lighting, Infrared and Ultraviolet Light, Filters, Machine Vision

Software, Machine Vision Automation, Integration of Machine

Vision Components

Digital Image Processing for Machine Vision Applications

Preprocessing., Image Filtering, Normalized Box Filter Gaussian

Filter Bilateral Filter, Comparison of Filter Techniques, Sub

sampling/Scaling Histogram, Image Segmentation, Threshold-

Based Segmentation Edge-Based Segmentation First-Order

Derivative Edge Detection. Second-Order Derivative Operators,

Comparison of Edge Detection Techniques, Region-Based

Segmentation Region Growing Methods, Region Split and Merge

Method, Morphological Image Processing: Dilation, Erosion,

Opening, Closing, Hit-or-Miss transformation, Object Recognition.

Template Matching. Blob Analysis

Motion Analysis

Differential motion Analysis, Optical Flow, Analysis based on

correspondence of interest points, Detection of specific motion

Patterns, Video Tracking

Emerging Trends in Machine Vision

6.1

History of Industrial Revolution(s), Machine Vision and

Industry 4.0, Emerging Vision Trends in Manufacturing, 3D

Imaging, Emerging Vision Trends in Manufacturing,

6.2

Applications in Machine/ Computer Vision: Face detection,

face recognition, eigen faces, car on roads

Textbooks:

Sheila Anand and L.Priya A Guide for MacTaylor &

Francis Inc, Imprint CRC Press Inc, Dec 2019

Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, Pearson

Carsten Stegar, Markus Ulrich, and Christian Wiedemann Machine Vision

Algorithms and ApplicationsSecond completely Revised and Enlarged Edition

Milan Sonka, Vaclav Hlavac, Roger Boyle, Image Processing Analysis and Machine

Vision, Second Edition, Cengage Learning.

References:

Chiranji Lal Chowdhary, Mamoun Alazab, Ankit Chaudhary, SaqibHakak and Thippa

Reddy Gadekallu Computer Vision and Recognition Systems Using Machine and

Deep Learning Approaches, Fundamentals, technologies and  , IET

COMPUTING SERIES 42

Joe Minichino Joseph Howse Learning OpenCV 3 Computer Vision with Python

Second Edition, Packt Publishing Ltd.

Alexander Handbook of Machine and Computer Vision The Guide for

Developers and Users,

Assessment:

Internal Assessment:

Assessment consists of two class tests of 20 marks each. The first-class test is to be

conducted when approx. 40% syllabus is completed and second class test when

additional40% syllabus is completed. Duration of each test shall be one hour.

End Semester Theory Examination:

Question paper will consist of 6 questions, each carrying 20 marks.

The students need to solve a total of 4 questions.

Question No.1 will be compulsory and based on the entire syllabus.

Remaining question (Q.2 to Q.6) will be selected from all the modules.

Useful Links

https://nptel.ac.in/courses/108103174

https://www.coursera.org/learn/introduction-computer-vision-watson-opencv

https://www.udacity.com/course/introduction-to-computer-vision--ud810

https://onlinecourses.nptel.ac.in/noc21_ee23/preview

Course Code

Course Title

Credit

CSDC7012

Cyber Security

Prerequisite: Computer Networks, Databases

Course Objectives:

To understand the need for Cyber Security Awareness.

To understand the Flow and Methodology of an attack

To learn and explore various Static and Web vulnerability analysis tools

To understand the various IPR, Privacy and Security compliances

Course Outcomes:

Understand the need of Cyber Security and awareness of existing law infrastructure

Illustrate the various tools and techniques used by attackers to launch their attacks

Appraise various mechanisms of conducting system vulnerability analysis

Discuss various web application vulnerability scanning techniques

Identify the various network defense methodologies

Describe the various Privacy and standard compliances

Module

Content

Hrs

Introduction to Cyber Crime and Attack Psychology

1.1

1.2

Cyber Crime: Need for Cyber Security, Cybercrime definition,

Types of Cybercrime. Classifications of cybercrime, Hackers,

Crackers and cyber criminals Indian IT ACT 2008 and its

amendments.

Attack Psychology: How cyber criminals plan the attacks, Social

Engineering, Cyber stalking, Cyber café and Cybercrimes,

Botnets, Attack vector, Attacks on Mobile.

Threat and Vulnerability Landscape

Goals of Security, Vulnerability vs Threat, What is Privacy,

Anonymity and pseudonymity, The Vulnerability Landscape,

Threat Modeling and risk Assessment, The Zero Trust model,

Spyware, Adware, Scareware , Browser Hijacking, Phishing vs

Vishing vs SMShing, Doxing and Spamming,Social Engineering

attack.

System Vulnerability Scanning and Network Reconnaissance

Password Cracking, Key loggers and Spywares, Virus and Worms,

Steganography, DoS and DDoS Attacks, SQL Injection, Buffer

Overflow, Phishing, Identity Theft (ID Theft),network information

gathering, vulnerability scanning, Open Port Identification, Banner

Grabbing Techniques, Vulnerability probing.

Web Application Security

OWASP, Web Security Considerations, Management, Cookies,

SSL, HTTPS, SSH, Privacy on Web, Web Browser Attacks,

Account Harvesting, Web Bugs, Clickjacking, Cross-Site Request

Forgery, Session Hijacking and Management, Phishing and

Pharming Techniques, Web Service Security, OAuth 2.0

Network Security and Defense Tools

Network security: Cyber Hygine, Firewalls vs Packet Filters,

Stateless vs Statefull Firewalls, Intrusion Detection System and its

types, Honeypots, Demilitarized Zones, Network Address

Translation (NAT), Port Forwarding. Virtual Private Networks,

Email Security(GPG Encryption), Registry Settings for Mobile

Devices

Information Security Privacy and Standard Compliances

HIPPA, FISMA, PCI DSS, GDPR, Intellectual Property Aspect of

Cyber Law, Creative Commons Library, Data Protection Laws in

India.

Textbooks:

William Stallings, Computer Security Principles and Practice, , Sixth Edition, Pearson

Education

Charles P. Pfleeger Security in Computing, , Fifth Edition, Pearson Education

The Complete Cyber Security Course -Volume 1- Nathan House

Eric Cole, Network Security Bible, Second Edition, Wiley

References:

Nina Godbole, Sunit Belapure, Cyber Security, Wiley India, New Delhi

The Information technology Act, 2000; Bare Act- Professional Book Publishers,

Build your own Security Lab, Wiley India

Computer Security, Third Edition, Wiley

Digital References:

Virtual Penetration Testing Labs- https://pentesterlab.com

OWASP- https://owasp.org/

DVWA- https://dvwa.co.uk

FISMA - https://csrc.nist.gov/projects/risk-management/fisma-background

PCI DS https://www.itgovernance.eu/blog/en/a-guide-to-the-4-pci-dss-compliance-levels

Assessment:

Internal Assessment:

Assessment consists of two class tests of 20 marks each. The first class test is to be conducted

when approx. 40% syllabus is completed and second class test when additional 40% syllabus is

completed. Duration of each test shall be one hour.

End Semester Theory Examination:

Question paper will comprise of total six questions.

All question carries equal marks

Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3

then part (b) will be from any module other than module 3)

Only Four question need to be solved.

In question paper weightage of each module will be proportional to number of respective

lecture hours as mention in the syllabus.

Course Code

Course Name

Credit

CSDC7013

Natural Language Processing

Pre-requisite: Theory of Computer Science, System Programming &Compiler Construction

Course Objectives: The course aims

To define natural language processing and to learn various stages of natural language

processing.

To describe basic concepts and algorithmic description of the main language levels:

Morphology, Syntax, Semantics, and Pragmatics &Discourse analysis.

To design and implement various language models and POS tagging techniques.

To design and learn NLP applications such as Information Extraction, Question answering.

To design and implement applications based on natural language processing.

Course Outcomes:Students will be able

To describe the field of natural language processing.

To design language model for word level analysis for text processing.

To design various POS tagging techniques and parsers.

To design, implement and test algorithms for semantic and pragmatic analysis.

To formulate the discourse segmentation and anaphora resolution.

To apply NLP techniques to design real world NLP applications.

Module

Detailed Content

Hours

1.1

Introduction to NLP

Origin & History of NLP; Language, Knowledge and Grammar in

language processing; Stages in NLP;Ambiguities and its types in

English and Indian Regional Llanguages; Challenges of

NLP;Applications of NLP

1.2

Self-Learning topics: Variety types of tools for regional languages

pre-processing and other functionalities

2.1

Word Level Analysis

Basic Terms: Tokenization, Stemming, Lemmatization; Survey of

English Morphology, Inflectional Morphology, Derivational

Morphology; Regular expression with types;

Morphological Models: Dictionary lookup, finite state morphology;

Morphological parsing with FST (Finite State Transducer);Lexicon

free FST Porter Stemmer algorithm; Grams and its variation: Bigram,

Trigram; Simple (Unsmoothed) N-grams;

N-gram Sensitivity to the Training Corpus; Unknown Words: Open

versus closed vocabulary tasks; Evaluating N-grams: Perplexity;

Smoothing: Laplace Smoothing, Good-Turing Discounting;

2.2

Self-Learning topics: Noisy channel models, various edit distance,

Advance Issues in Language Modelling

3.1

Syntax analysis

Part-Of-Speech tagging(POS); Tag set for English (Upenn Treebank);

Difficulties /Challenges in POS tagging; Rule-based, Stochastic and

Transformation-based tagging; Generative Model: Hidden Markov

Model (HMM Viterbi) for POS tagging;

Issues in HMM POS tagging; Discriminative Model: Maximum

Entropy model, Conditional random Field (CRF);Parsers: Top down

and Bottom up; Modelling constituency; Bottom Up Parser: CYK,

PCFG (Probabilistic Context Free Grammar), Shift Reduce Parser; Top

Down Parser: Early Parser, Predictive Parser

3.2

Self-Learning topics: Evaluating parsers, Parsers based language

modelling, Regional languages POS tree banks

4.1

Semantic Analysis

Introduction, meaning representation; Lexical Semantics; Corpus

study; Study of Various language dictionaries like WorldNet, Babelnet;

Relations among lexemes & their senses Homonymy, Polysemy,

Synonymy, Hyponymy; Semantic Ambiguity;

Word Sense Disambiguation (WSD); Knowledge based approach(



List),Introduction to Semi-supervised method (Yarowsky)

Unsupervised (Hyperlex)

4.2

Self-Learning topics: Dictionaries for regional languages,

Distributional Semantics, Topic Models

5.1

Pragmatic & Discourse Processing

Discourse: Reference Resolution, Reference Phenomena, Syntactic &

Semantic constraint on coherence; Anaphora Resolution using Hobbs

and Cantering Algorithm

5.2

Self-Learning topics: Discourse segmentation, Conference resolution

6.1

Applications of NLP

Case studies on (preferable in regional language):Machine

translation; Text Summarization; Sentiment analysis; Information

retrieval; Question Answering system

6.2

Self-Learning topics: Applications based on Deep Neural

Network with NLP such as LSTM network, Recurrent Neural

network etc.

Textbooks:

Daniel Jurafsky, James H. and Martin, Speech and Language Processing, Second Edition,

Prentice Hall, 2008.

Christopher D.Manning and HinrichSchutze, Foundations of Statistical Natural Language

Processing, MIT Press, 1999.

References:

Siddiqui and Tiwary U.S., Natural Language Processing and Information Retrieval, Oxford

University Press, 2008.



from theory to practice, IBM Press, 2013.



Linguistics and Natural Language Processing, John Wiley and Sons, 2012.



Second Edition, Chapman and Hall/CRC Press, 2010.

Niel J le Roux and SugnetLubbe, A step by step tutorial: An introduction into R

application and programming.

Steven Bird, Ewan Klein and Edward Loper, Natural language processing with Python:



Digital References :

http://www.cse.iitb.ac.in/~cs626-449

http://cse24-iiith.virtual-labs.ac.in/#

https://nptel.ac.in/courses/106105158

Assessment:

Internal Assessment:

Assessment consists of two class tests of 20 marks each. The first-class test is to be conducted

when approx. 40% syllabus is completed and second class test when additional 40% syllabus is

completed. Duration of each test shall be one hour.

End Semester Theory Examination:

Question paper will consist of 6 questions, each carrying 20 marks.

The students need to solve a total of 4 questions.

Question No.1 will be compulsory and based on the entire syllabus.

Remaining question (Q.2 to Q.6) will be selected from all the modules.

Course Code

Course Name

Credit

CSDC7021

Virtual and Augmented Reality

Prerequisite: Computer Graphics

Course Objectives: The course aims:

To understand the need and significance of Virtual Reality.

To explore the concepts of Virtual reality and develop 3D virtual environments.

To understand the technical and engineering aspects of virtual reality systems.

To analyze various techniques for applying virtual reality.

To provide a foundation to the fast growing field of AR and make the students aware of the

various AR devices.

Course Outcomes: Learners will be able to

Describe how VR systems work and list the applications of VR

Elaborate geometric presentationof the virtual world and its operations.

Explain the concepts of motion and tracking in VR systems.

Design and implementation of the hardware that enables VR systems tobe built.

Describe how AR systems work and analyze the hardware requirement of AR

Analyze and understand the working of various state of the art AR devices.

Module

Detailed Content

Hours

Introduction to Virtual Reality

What is virtual reality? ,The beginnings of VR , VR paradigms ,

Collaboration, Virtual reality systems, Representation ,User interaction

The Geometry of Virtual Worlds

Geometric Models, Changing Position and Orientation, Axis-Angle

Representations of Rotation, Viewing Transformations, Chaining the

Transformations

Motion in Real and Virtual Worlds

Velocities and Accelerations , The Vestibular System , Physics in the Virtual

World , Mismatched Motion and Vection

Applying Virtual Reality

Virtual reality: the medium, Form and genre, What makes an application a

good candidate for VR, Promising application fields, Demonstrated benefits

of virtual reality , More recent trends in virtual reality application

development, A framework for VR application development

Augmented Reality

Terminology, Simple augmented reality, Augmented reality as an emerging

technology, Augmented reality applications, Marker detection, Marker pose,

Marker types and identification: Template markers, 2D bar-code markers,

Imperceptible markers: Image markers, Infrared markers, Miniature markers,

Discussion on marker use, General marker detection application

AR Development & Applications

User interfaces, Avoiding physical contacts , Practical experiences with

head-mounted displays , Authoring and dynamic content ,AR applications

and future visions, How to design an AR application ,Technology adoption

and acceptance , Where to use augmented reality

Textbooks:

Virtual Reality, Steven M. LaValle, Cambridge University Press, 2016

Understanding Virtual Reality: Interface, Application and Design, William R Sherman and

 Kaufmann

Publishers, San Francisco, CA, 2002

Developing Virtual Reality Applications: Foundations of Effective Design, Alan B

Craig,William R Sherman and Jeffrey D Will, Morgan Kaufmann, 2009.

Theory and applications of marker-based augmented reality SanniSiltanen

References:

-

1484236178

Augmented Reality: Principles & Practice by Schmalstieg / Hollerer, Pearson Education

India;

First edition (12 October 2016),ISBN-10: 9332578494

- 9781491922835

Digital Useful Links

https://freevideolectures.com/course/3693/virtual-reality

https://www.vrlabacademy.com/

https://arvr.google.com/ar/

https://konterball.com/

Assessment:

Internal Assessment:

Assessment consists of two class tests of 20 marks each. The first-class test is to be conducted

when approx. 40% syllabus is completed and second class test when additional40% syllabus is

completed. Duration of each test shall be one hour.

End Semester Theory Examination:

Question paper will consist of 6 questions, each carrying 20 marks.

The students need to solve a total of 4 questions.

Question No.1 will be compulsory and based on the entire syllabus.

Remaining question (Q.2 to Q.6) will be selected from all the modules.

Course Code:

Course Title

Credit

CSDC7022

Blockchain

Prerequisite: Cryptography and System Security

Course Objectives:

Understand blockchain platforms and its terminologies.

Understand the use of cryptography required for blockchain.

Understand smart contracts, wallets, and consensus protocols.

Design and develop blockchain applications

Course Outcomes:

Explain blockchain concepts.

Apply cryptographic hash required for blockchain.

Apply the concepts of smart contracts for an application.

Design a public blockchain using Ethereum.

Design a private blockchain using Hyperledger.

Use different types of tools for blockchain applications.

Module

Content

Hrs

Introduction to Blockchain

1.1

What is a blockchain, Origin of blockchain (cryptographically secure

hash functions), Foundation of blockchain: Merkle trees

1.2

Components of blockchain, Block in blockchain, Types: Public,

Private, and Consortium, Consensus Protocol, Limitations and

Challenges of blockchain

Cryptocurrency

2.1

Cryptocurrency: Bitcoin, Altcoin, and Tokens (Utility and Security),

Cryptocurrency wallets: Hot and cold wallets, Cryptocurrency usage,

Transactions in Blockchain, UTXO and double spending problem

2.2

Bitcoin blockchain: Consensus in Bitcoin, Proof-of-Work (PoW),

Proof-of-Burn (PoB), Proof-of-Stake (PoS), and Proof-of-Elapsed

Time (PoET), Life of a miner, Mining difficulty, Mining pool and its

methods

Programming for Blockchain

3.1

Introduction to Smart Contracts, Types of Smart Contracts, Structure

of a Smart Contract, Smart Contract Approaches, Limitations of

Smart Contracts

3.2

Introduction to Programming: Solidity Programming  Basics,

functions, Visibility and Activity Qualifiers, Address and Address

Payable, Bytes and Enums, Arrays-Fixed and Dynamic Arrays,

Special Arrays-Bytes and strings, Struct, Mapping, Inheritance, Error

handling

3.3

Case Study  Voting Contract App, Preparing for smart contract

development

Public Blockchain

Introduction to Public Blockchain, Ethereum and its Components,

Mining in Ethereum, Ethereum Virtual Machine (EVM), Transaction,

Accounts, Architecture and Workflow, Comparison between Bitcoin

and Ethereum

Types of test-networks used in Ethereum, Transferring Ethers using

Metamask, Mist Wallet, Ethereum frameworks, Case study of

Ganache for Ethereum blockchain. Exploring etherscan.io and ether

block structure

Private Blockchain

5.1

Introduction, Key characteristics, Need of Private Blockchain, Smart

Contract in a Private Environment, State Machine Replication,

Consensus Algorithms for Private Blockchain - PAXOS and RAFT,

Byzantine Faults: Byzantine Fault Tolerant (BFT) and Practical BFT

5.2

Introduction to Hyperledger, Tools and Frameworks, Hyperledger

Fabric, Comparison between Hyperledger Fabric & Other

Technologies

5.3

Hyperledger Fabric Architecture, Components of Hyperledger Fabric:

MSP, Chain Codes, Transaction Flow, Working of Hyperledger

Fabric, Creating Hyperledger Network, Case Study of Supply Chain

Management using Hyperledger

Tools and Applications of Blockchain

Corda, Ripple, Quorum and other Emerging Blockchain Platforms,

Blockchain in DeFi: Case Study on any of the Blockchain Platforms.

Textbooks:

Blockchain Technology, Chandramouli Subramanian, Asha A. George, Abhillash K. A and

Meena Karthikeyen, Universities Press.

Mastering Ethereum, Building Smart Contract and Dapps, Andreas M. Antonopoulos Dr.



Imran Bashir, Mastering Blockchain: A deep dive into distributed ledgers, consensus

protocols, smart contracts, DApps, cryptocurrencies, Ethereum, and more, 3rd Edition, Packt

Publishing

References:

Blockchain for Beginners, Yathish R and Tejaswini N, SPD

Blockchain Basics, A non Technical Introduction in 25 Steps, Daniel Drescher, Apress.

Blockchain with Hyperledger Fabric,Luc Desrosiers, Nitin Gaur, Salman A. Baset,

Venkatraman Ramakrishna, Packt Publishing

Assessment:

Internal Assessment:

Assessment consists of two class tests of 20 marks each. The first class test is to be conducted

when approx. 40% syllabus is completed and second class test when additional 40% syllabus is

completed. Duration of each test shall be one hour.

End Semester Theory Examination:

Question paper will comprise a total of six questions.

All question carries equal marks

Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then

part (b) will be from any module other than module 3)

Only Four question need to be solved.

In question paper weightage of each module will be proportional to the number of respective

lecture hours as mention in the syllabus.

Digital Useful Links

Blockchain By Example, Bellaj Badr, Richard Horrocks, Xun (Brian) Wu, November 2018,

Implement decentralized blockchain applications to build scalable Dapps.

Blockchain for Business, https://www.ibm.com/downloads/cas/3EGWKGX7.

https://www.hyperledger.org/use/fabric

NPTEL: https://onlinecourses.nptel.ac.in/noc19_cs63/preview

Course Code

Course Name

Credit

CSDC7023

Information Retrieval

Prerequisite: Data structures and algorithms

Course Objectives: The course aims students :

To learn the fundamentals of Information Retrieval

To analyze various Information retrieval modeling techniques

To understand query processing and its applications

To explore the various indexing and scoring techniques

To assess the various evaluation methods

To analyze various information retrieval for real world application

Course Outcomes: Learner will be able to: -

Define and describe the basic concepts of the Information retrieval system.

Design the various modeling techniques for information retrieval systems.

Understand the query structure and various query operations

Analyzing the indexing and scoring operation in information retrieval systems

Perform the evaluation of information retrieval systems

Analyze various information retrieval for real world application

Module

Detailed Content

Hours

Introduction to Information Retrieval

1.1

Introduction to Information Retrieval, Basic Concepts,

Information Versus Data, Trends and research issues in

information retrieval.

1.2

The retrieval process, Information retrieval in the library, web and

digital libraries.

Modeling in Information Retrieval

2.1

Taxonomy of Information Retrieval models, Classic Information

Retrieval, Alternate set: Theoretical model, Alternative Algebraic

models, Alternative Probabilistic models

2.2

Structured text Retrieval models, Models for browsing

Query and Operations in Information Retrieval

3.1

Query structures, Keyboard based querying, Pattern matching,

Structured queries

3.2

User relevance feedback, Automatic local analysis, Automatic global

analysis

Indexing and Scoring in Information Systems

4.1

Introduction, Inverted Files, Other Indices for Text, Boolean queries

and Introduction to Sequential searching

4.2

Scoring, term weighting and the vector space model, Parametric and

zone indexes, Weighted zone scoring, Learning weights, The optimal

weight, Term frequency and weighting, Inverse document frequency,

Tf-idf weighting.

The vector space model for scoring, Queries as vectors, Computing

vector scores, Efficient scoring and ranking, Inexact top K document

retrieval

Evaluation of Information Retrieval Systems

5.1

Information retrieval system evaluation, Standard test collections,

Evaluation of unranked retrieval sets, Evaluation of ranked retrieval

results, Assessing and justifying the concept of relevance

5.2

System quality and user utility, System issues, Refining a deployed

system

Applications of Information Retrieval Systems

6.1.

Introduction to Multimedia Information Retrieval

6.2

Introduction to Distributed Information Retrieval

Textbooks:

Modern information retrieval, Baeza-Yates, R. and Ribeiro-Neto, B., 1999. ACM press.

Introduction to Information Retrieval By Christopher D. Manning and PrabhakarRaghavan,

Cambridge University Press

Information Storage & Retrieval By Robert Korfhage  John Wiley & Sons

References:

Storage Network Management and Retrieval, VaishaliKhairnar

Introduction to Modern Information Retrieval. G.G. Chowdhury. NealSchuman

Natural Language Processing and Information Retrieval by Tanveer Siddiqui, U.S Tiwarey

Useful Digital Links

https://web.stanford.edu/class/cs276/

https://www.coursera.org/learn/text-retrieval

Assessment:

Internal Assessment:

Assessment consists of two class tests of 20 marks each. The first-class test is to be conducted

when approx. 40% syllabus is completed and second class test when additional 40% syllabus

is completed. Duration of each test shall be one hour.

End Semester Theory Examination:

Question paper will consist of 6 questions, each carrying 20 marks.

The students need to solve a total of 4 questions.

Question No.1 will be compulsory and based on the entire syllabus.

Remaining question (Q.2 to Q.6) will be selected from all the modules.

Lab Code

Lab Name

Credit

CSL70011

Machine Learning Lab

Prerequisite: Data Structures, Analysis of Algorithms

Lab Objectives:

To introduce the basic concepts and techniques of Machine Learning.

To acquire in depth understanding of various supervised and unsupervised algorithms

To be able to apply various ensemble techniques for combining ML models.

To demonstrate dimensionality reduction techniques.

Lab Outcomes: At the end of the course, the students will be able to

To implement an appropriate machine learning model for the given application.

To implement ensemble techniques to combine predictions from different models.

To implement the dimensionality reduction techniques.

Suggested List of Experiments

Sr. No.

Title of Experiment

To implement Linear Regression.

To implement Logistic Regression.

To implement Ensemble learning (bagging/boosting)

To implement multivariate Linear Regression.

To implement SVM

To implement PCA/SVD/LDA

To implement Graph Based Clustering

To implement DB Scan

To implement CART

To implement LDA

Term Work:

Term work should consist of 6 experiments.

Journal must include one mini project/case study on any machine learning application.

The final certification and acceptance of term work ensures the satisfactory performance of

laboratory work and minimum passing marks in term work.

Total 25 Marks (Experiments & Assignments: 15-marks, Attendance: 05-marks, mini project:

05-marks)

Oral & Practical exam.

Based on the entire syllabus CSC7011 Machine Learning and CSL7011: Machine Learning

Lab

Lab Code

Lab Name

Credit

CSL7012

Big Data Analytics Lab

Prerequisite: C Programming Language.

Lab Objectives: Students will be able to

Solve Big Data problems using Map Reduce Technique and apply to various algorithms.

Identify various types of NoSQL databases and execute NOSQL commands

Understand implementation of various analytic techniques using Hive/PIG/R/Tableau, etc.

Apply streaming analytics to real time applications.

Lab Outcomes:

To interpret business models and scientific computing paradigms, and apply software tools for

big data analytics.

To implement algorithms that uses Map Reduce to apply on structured and unstructured data

To perform hands-on NoSql databases such as Cassandra, HadoopHbase, MongoDB, etc.

To implement various data streams algorithms.

To develop and analyze the social network graphs with data visualization techniques.

Suggested List of Experiments

(Select a case study and perform the experiments 1 to 8.).

Star (*) marked experiments are compulsory.

Sr. No.

Name of the Experiment

Hadoop HDFS Practical:

-HDFS Basics, Hadoop Ecosystem Tools Overview.

-Installing Hadoop.

-Copying File to Hadoop.

-Copy from Hadoop File system and deleting file.

-Moving and displaying files in HDFS.

-Programming exercises on Hadoop

Use of Sqoop tool to transfer data between Hadoop and relational database servers.

a. Sqoop - Installation.

b. To execute basic commands of Hadoop eco system componentSqoop.

To install and configure MongoDB/ Cassandra/ HBase/ Hypertable to execute NoSQL

commands

Experiment on Hadoop Map-Reduce:

-Write a program to implement a word count program using MapReduce.

Experiment on Hadoop Map-Reduce:

-Implementing simple algorithms in Map-Reduce: Matrix multiplication, Aggregates,

Joins, Sorting, Searching, etc

Create HIVE Database and Descriptive analytics-basic statistics.

Data Stream Algorithms (any one):

- Implementing DGIM algorithm using any Programming Language

- Implement Bloom Filter using any programming language

Implement Flajolet Martin algorithm using any programming language

Social Network Analysis using R (for example: Community Detection Algorithm)

Data Visualization using Hive/PIG/R/Tableau/.

Exploratory Data Analysis using Spark/ Pyspark.

11*

Mini Project: One real life large data application to be implemented (Use standard

Datasets available on the web).

- Streaming data analysis  use flume for data capture, HIVE/PYSpark for analysis of

twitter data, chat data, weblog analysis etc.

- Recommendation System (for example: Health Care System, Stock Market

Prediction, Movie Recommendation, etc.)

SpatioTemporal DataAnalytics

Useful Links:

https://www.coursera.org/learn/hadoop#syllabus

https://www.coursera.org/learn/introduction-mongodb#syllabus

https://www.coursera.org/learn/data-visualization-tableau?specialization=data-visualization#syllabus

https://www.coursera.org/learn/introduction-to-big-data-with-spark-hadoop#syllabus

Term Work:

Term work should consist of 8 experiments.

The final certification and acceptance of term work ensures satisfactory performance of

laboratory work and minimum passing marks in term work.

The final certification and acceptance of term work ensures satisfactory performance of

laboratory work and minimum passing marks in term work. Total 25 Marks (Experiments:

15-marks, Attendance Theory & Practical: 05-marks, Assignment: 05-marks)

Oral & Practical exam

Based on the entire syllabus of and CSC702 : Big Data Analytics and CSL702 Big Data

Analytics Lab

Lab Code

Lab Name

Credit

CSDL7011

Machine Vision Lab

Prerequisite: Computer Graphics, Image Processing, Python

Lab Objectives:

To perform basic image processing operations

To explore different preprocessing technique

To develop application related to Machine vision

To detect and recognize objects

Lab Outcomes:

Students will be able to read image and video file, perform different processing

Students will be able to do edge detection ,depth estimation

Students will be able to choose appropriate algo for segmentation

Students will be able to implement object detection technique

Suggested Experiments: Students are required to complete at least 8 experiments.

Sr.No.

Name of the Experiment

Handling Files, Cameras, and GUIs

Basic I/O scripts ,Reading/writing an image file ,Converting between an image and raw

bytes ,Accessing image data with numpy.array ,Reading/writing a video file ,Capturing

camera frames,

Displaying images in a window, Displaying camera frames in a window

Processing Images with OpenCV 3

Converting between different color spaces,

The Fourier Transform, High pass filter, Low pass filter,

Edge detection with Canny,

Contour detection,

Contours  bounding box, minimum area rectangle, and minimum enclosing circle

,Contours  convex contours and the Douglas-Peucker algorithm

,Line and circle detection

Depth Estimation

Capturing frames from a depth camera

Creating a mask from a disparity map

Masking a copy operation

Depth estimation with a normal camera

Object segmentation using the Watershed and GrabCut algorithms

Example of foreground detection with GrabCut

Image segmentation with the Watershed algorithm

Detecting and Recognizing Faces

Conceptualizing Haar cascades

Getting Haar cascade data

Using OpenCV to perform face detection

Performing face detection on a still image

Performing face detection on video

Performing face recognition

Generating the data for face recognition

Recognizing faces

Preparing the training data

Loading the data and recognizing faces

Performing an Eigenfaces recognition

Retrieving Images and Searching

Using Image Descriptors ,

Feature detection algorithms,

Defining features

Detecting features  corners

Feature extraction and description using DoG and SIFT

Anatomy of a keypoint

Detecting and Recognizing Objects

Object detection and recognition techniques

HOG descriptors

The scale issue

The location issue

Non-maximum (or non-maxima) suppression

Support vector machines

People detection

Creating and training an object detector

Bag-of-words

BOW in computer vision

Detecting cars in a scene

Reference &Useful Links:

Learning OpenCV 3 Computer Vision with Python Second Edition, by Joe Minichino Joseph

Howse Published by Packt Publishing Ltd.

http://iitk.ac.in/ee/computer-vision-lab

https://nptel.ac.in/courses/108103174

https://docs.opencv.org/3.4/d9/df8/tutorial_root.html

Term Work:

Term work should consist of 8 experiments.

Journal must include at least 2 assignments.

The final certification and acceptance of term work ensures that satisfactory performance of

laboratory work and minimum passing marks in term work.

Total 25 Marks (Experiments: 15-marks, Attendance Theory & Practical: 05-marks,

Assignments: 05-marks)

Lab Code

Lab Name

Credit

CSDL7012

Cyber Security Lab

Prerequisite: Computer Network

Lab Objectives:

To study and implement the flow and methodology of an attack

To explore various static and web vulnerability analysis tools

To study and implement the various Privacy and Security compliances

Lab Outcomes: At the end of the course, the students will be able to

Demonstrate the concept of various Privacy and standard compliances

Apply various tools for conducting system vulnerability analysis

Understand the various web application vulnerability scanning techniques

Apply the various network defense techniques

Demonstrate the concept of various techniques used by attackers to launch their attacks

Suggested List of Experiments

Sr. No.

Title of Experiment

Study and implement phishing using website cloning.

Recommended Tool: HTTrack Website Copier.

Static Code Analysis using open source tools.

Recommended Tool: Flawfinder Python Distribution

Web Application Vulnerability Scanning and Auditing using open source tools.

Recommended Tools: Nikto / Wapiti / Burpsuite (Kali Linux/ Windows)

Study and exploit database flaws and vulnerabilities using SQL Injection Attack.

Recommended Tool: SQLMap (Kali Linux / Windows)

Study and Implement Packet Sniffing using Open Source Tools.

Recommended Tools: Wireshark, TCP Dump

Study and implement Session Hijacking / Man in the Middle (MiTM) attack in a

controlled virtual environment.

Recommended Tools: Ettercap / Bettercap

Penetration Testing and Vulnerability Exploitation

Recommended Tool: Metasploit (Kali Linux)

Study and Implement Cross Site Request Forgery in a controlled virtual environment.

Recommended Tool: Damn Vulnerable Web App (DVWA) Web Server

Exploring Authentication, Authorization and Access Control (AAA) .

Recommended Tool: Cisco Packet Tracer Student Edition, TACACS

Study and Implement Firewalls using IP tables.

Recommended Tool: Kali Linux, IPTables, Oraclebox/VmWare

Study and Implement a Network Intrusion Detection System (NIDS)

Recommended Tool: Kali Linux, SNORT, Oraclebox/VmWare

Study and Implement a Host Based Intrusion Detection System (HIDS)

Recommended Tool: Kali Linux, OSSEC, Oraclebox/VmWare

Study and Implement Email Security using PGP Encryption

Recommended Tool: GnuPG (Open Source Implementation of PGP)

Study and Explore Malware/Keylogger Detection

Recommended Tool: netstat, TCPView

Study and Explore the Creative Commons Library

Term Work:

Term work should consist of 10 experiments.

Journal must include at least 2 assignments on content of theory and   



The final certification and acceptance of term work ensures that satisfactory performance of

laboratory work and minimum passing marks in term work.

Total 25 Marks (Experiments: 15-marks, Attendance Theory& Practical: 05-marks,

Assignments: 05-marks)

Lab Code

Lab Name

Credit

CSDL7013

Natural Language processing Lab

Prerequisite: Java/Python

Lab Objectives: The course aims

To understand the key concepts of NLP.

To learn various phases of NLP.

To design and implement various language models and POS tagging techniques.

To understand various NLP Algorithms

To learn NLP applications such as Information Extraction, Sentiment Analysis, Question

answering, Machine translation etc.

To design and implement applications based on natural language processing

Lab Outcomes:Learners will be able

Apply various text processing techniques.

Design language model for word level analysis.

Model linguistic phenomena with formal grammar.

Design, implement and analyze NLP algorithms.

To apply NLP techniques to design real world NLP applications such as machine translation,

sentiment analysis, text summarization, information extraction, Question Answering system

etc.

Implement proper experimental methodology for training and evaluating empirical NLP

systems.

Suggested List of Experiments

(Select a case study and perform the experiments 1 to 8.).

Star (*) marked experiments are compulsory.

Sr. No.

Name of the Experiment

Study various applications of NLP and Formulate the Problem Statement for Mini

Project based on chosen real world NLP applications:

[Machine Translation, Text Categorization, Text summarization, chat Bot, Plagarism,

Spelling & Grammar checkers, Sentiment / opinion analysis, Question answering,

Personal Assistant, Tutoring Systems, etc.]

Apply various text preprocessing techniques for any given text : Tokenization and

Filtration & Script Validation.

Apply various other text preprocessing techniques for any given text : Stop Word

Removal, Lemmatization / Stemming.

Perform morphological analysis and word generation for any given text.

Implement N-Gram model for the given text input.

Study the different POS taggers and Perform POS tagging on the given text.

Perform Chunking for the given text input.

Implement Named Entity Recognizer for the given text input.

Implement Text Similarity Recognizer for the chosen text documents.

Exploratory data analysis of a given text (Word Cloud)

Mini Project Report: For any one chosen real world NLP application.

Implementation and Presentation of Mini Project

Study various applications of NLP and Formulate the Problem Statement for Mini

Project based on chosen real world NLP applications:

[Machine Translation, Text Categorization, Text summarization, chat Bot, Plagarism,

Spelling & Grammar checkers, Sentiment / opinion analysis, Question answering,

Personal Assistant, Tutoring Systems, etc.]

Term Work:

Study various applications of NLP and Formulate the Problem Statement for Mini Project

based on chosen real world NLP applications:

[Machine Translation, Text Categorization, Text summarization, chat Bot, Plagarism, Spelling

& Grammar checkers, Sentiment / opinion analysis, Question answering, Personal Assistant,

Tutoring Systems, etc.]

Apply various text preprocessing techniques for any given text: Tokenization and Filtration &

Script Validation.

Apply various other text preprocessing techniques for any given text: Stop Word Removal,

Lemmatization / Stemming.

Lab Code

Lab Name

Credit

CSDL7021

Augmented and Virtual Reality Lab

Prerequisite: Computer Graphics, Image Processing, Python

Lab Objectives:

To perform installation of Unity

To explore working of VR Gadget

To develop scene VR application

To track objects in virtual environment

Lab Outcomes: Learners will be able to

Setup VR development environment

Use HTC Vive/ Google Cardboard/ Google Daydream and Samsung gear VR.

Develop VR scene and place object

Work with Augmented Faces features.

Suggested Experiments: Students are required to complete at least 6 experiments.

Sr.

No.

Name of the Experiment

Installation of Unity and Visual Studio, setting up Unity for VR development,

understanding documentation of the same.

Demonstration of the working of HTC Vive, Google Cardboard, Google Daydream

and Samsung gear VR.

Develop a scene in Unity that includes:

i. a cube, plane and sphere, apply transformations on the 3 game objects.

ii. add a video and audio source

Develop a scene in Unity that includes a cube, plane and sphere. Create a new

material and texture separately for three Game objects. Change the colour, material

and texture of each Game object separately in the scene. Write a C# program in

visual studio to change the colour and material/texture of the game objects

dynamically on button click.

Develop a scene in Unity that includes a sphere and plane . Apply Rigid body

component, material and Box collider to the game Objects. Write a C# program to

grab and throw the sphere using vr controller.

Develop a simple UI(User interface ) menu with images, canvas, sprites and button.

Write a C# program to interact with UI menu through VR trigger button such that on

each successful trigger interaction display a score on scene .

Place a three-dimensional ARCore pawn on detected AR plane surfaces

Using the Augmented Faces feature in your own apps.

Term Work:

Term work should consist of 6 experiments.

Journal must include at least 2 assignments.

The final certification and acceptance of term work ensures that satisfactory

performance of laboratory work and minimum passing marks in term work.

Total 25 Marks (Experiments: 15-marks, Attendance Theory & Practical: 05-marks,

Assignments: 05-marks)

Lab Code

Lab Name

Credit

CSDL7022

Blockchain Lab

Prerequisite:

Cryptography and Network Security

Lab Objectives:

To explore Blockchain concepts.

To implement public and private Blockchain.

To create applications using Blockchain.

Lab Outcomes:

At the end of the course, the students will be able to

Creating Cryptographic hash using merkle tree.

Design Smart Contract using Solidity.

Implementing ethereum blockchain using Geth.

Demonstrate the concept of blockchain in real world application.

Suggested List of Experiments

Sr. No.

Title of Experiment

Cryptography in Blockchain, Merkle root tree hash

Creating Smart Contract using Solidity and Remix IDE.

Creating Transactions using Solidity and Remix IDE

Embedding wallet and transaction using Solidity

Blockchain platform ethereum using Geth.

Blockchain platform Ganache.

Case Study on Hyperledger

Case Study on Other Blockchain platforms.

Creating a blockchain Application

Term Work:

Term work should consist of 8 experiments and one mini project.

Journal must include at least 2 assignments on content of theory and practical of

Blockchain Lab

The final certification and acceptance of term work ensures satisfactory performance of

laboratory work and minimum passing marks in term work.

Total 25 Marks (Experiments: 15-marks, Attendance Theory & Practical: 05-marks,

Assignments: 05-marks)

Lab Code

Lab Name

Credit

CSDL7023

Information Retrieval Lab

Prerequisite: Java, Python

Lab Objectives:

To understand the formation of queries.

To implement the various modeling techniques for IR.

To execute query expansion techniques.

To evaluate Information retrieval systems.

Lab Outcomes: Students will be able :-

To frame queries for information retrieval

To implement modeling techniques

To perform query expansion techniques

To demonstrate evaluation techniques for IR

Suggested Experiments: Students are required to perform any 5 experiments from the

suggested list along with a case study (* indicates compulsory experiment)

Sr.

No.

Name of the Experiment

To understand the query structure and execute various structured queries

To implement any IR modeling technique

To implement Pattern matching method used for IR

To execute query expansion technique (Local/Global)

To design inverted indices for any information retrieval model

To implement tf-id weighting

To evaluate the system/application under study

To understand the Case Study and generate a report for the same

Term Work:

Term work should consist of 5 experiments and 1 case study

Journal must include at least 2 assignments.

The final certification and acceptance of term work ensures satisfactory performance of

laboratory work and minimum passing marks in term work.

Total: 25 Marks (Experiments: 10-marks, Case study - 5 marks Attendance Theory

& Practical: 05-marks, Assignments: 05-marks)

Course Code

Course Name

Credit

CSP701

Major Project 1

Course Objectives:

The project work facilitates the students to develop and prove Technical, Professional and

Ethical skills and knowledge gained during graduation program by applying them from problem

identification, analyzing the problem and designing solutions.

Course Outcomes: Learner will able

To develop the understanding of the problem domain through extensive review of

literature.

To Identify and analyze the problem in detail to define its scope with problem specific

data.

To know various techniques to be implemented for the selected problem and related

technical skills through feasibility analysis.

To design solutions for real-time problems that will positively impact society and

environment..

To develop clarity of presentation based on communication, teamwork and leadership

skills.

To inculcate professional and ethical behavior.

Guidelines:

1. Project Topic Selection and Allocation:

 Project topic selection Process to be defined and followed:

o Project orientation can be given at the end of sixth semester.

o Students should be informed about the domain and domain experts whose

guidance can be taken before selecting projects.

o          nferences/

journals like IEEE, Elsevier, ACM etc. which are not more than 3 years old for

review of literature.

o Students can certainly take ideas from anywhere, but be sure that they should

evolve them in the unique way to suit their project requirements. Students can be

informed to refer Digital India portal, SIH portal or any other hackathon portal for

problem selection.

 Topics can be finalized with respect to following criterion:

o Topic Selection: The topics selected should be novel in nature (Product based,

Application based or Research based) or should work towards removing the

lacuna in currently existing systems.

o Technology Used: Use of latest technology or modern tools can be encouraged.

o Students should not repeat work done previously (work done in the last three

years).

o Project work must be carried out by the group of at least 2 students and maximum

o The project work can be undertaken in a research institute or

organization/Industry/any business establishment. (out-house projects)

o The project proposal presentations can be scheduled according to the domains

and should be judged by faculty who are expert in the domain.

o Head of department and senior staff along with project coordinators will take

decision regarding final selection of projects.

o Guide allocation should be done and students have to submit weekly progress

report to the internal guide.

o Internal guide has to keep track of the progress of the project and also has to

maintain attendance report. This progress report can be used for awarding term

work marks.

o In case of industry/ out-house projects, visit by internal guide will be preferred

and external members can be called during the presentation at various levels

2. Project Report Format:

At the end of semester, each group needs to prepare a project report as per the guidelines

issued by the University of Mumbai.

A project report should preferably contain at least following details:

o Abstract

o Introduction

o Literature Survey/ Existing system

o Limitation Existing system or research gap

o Problem Statement and Objective

o Proposed System

o Analysis/Framework/ Algorithm

o Design details

o Methodology (your approach to solve the problem) Proposed System

o Experimental Set up

o Details of Database or details about input to systems or selected data

o Performance Evaluation Parameters (for Validation)

o Software and Hardware Set up

o Implementation Plan for Next Semester

o Timeline Chart for Term1 and Term-II (Project Management tools can be

used.)

o References

Desirable

Students can be asked to undergo some Certification course (for the technical skill set that

will be useful and applicable for projects.)

3. Term Work:

Distribution of marks for term work shall be done based on following:

o Weekly Log Report

o Project Work Contribution

o Project Report (Spiral Bound) (both side print)

o Term End Presentation (Internal)

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

above aspects.

4. Oral and Practical:

Oral and Practical examination (Final Project Evaluation) of Project 1 should be

conducted by Internal and External examiners approved by University of Mumbai at the

end of the semester.

Suggested quality evaluation parameters are as follows:

o Quality of problem selected

o Clarity of problem definition and feasibility of problem solution

o Relevance to the specialization / industrial trends

o Originality

o Clarity of objective and scope

o Quality of analysis and design

o Quality of written and oral presentation

o Individual as well as team work

Course Code:

Course Title

Credit

CSC801

Distributed Computing

Prerequisite: Computer Networks and Operating Systems.

Course Objectives:

To provide students with contemporary knowledge in distributed systems.

To explore the various methods used for communication in distributed systems.

To provide skills to measure the performance of distributed synchronization algorithms.

To provide knowledge of resource management, and process management including

process migration.

To learn issues involved in replication, consistency, and file management.

To equip students with skills to analyze and design distributed applications.

Course Outcomes: At the end of the course students will be able to

Demonstrate the knowledge of basic elements and concepts related to distributed system

technologies.

Illustrate the middleware technologies that support distributed applications such as RPC,

RMI and Object-based middleware.

Analyze the various techniques used for clock synchronization, mutual exclusion and

deadlock.

Demonstrate the concepts of Resource and Process management.

Demonstrate the concepts of Consistency, Replication Management and fault Tolerance.

Apply the knowledge of Distributed File systems in building large-scale distributed

applications.

Module

Content

Hrs

Introduction to Distributed Systems

1.1

Characterization of Distributed Systems: Issues, Goals, Types of distributed

systems, Grid and Cluster computing Models, Hardware and Software Concepts:

NOS, DOS.

1.2

Middleware: Models of middleware, Services offered by middleware.

Communication

2.1

Interprocess communication (IPC): Remote Procedure Call (RPC), Remote

Method Invocation (RMI).

2.2

Message-Oriented Communication, Stream Oriented Communication, Group

Communication.

Synchronization

3.1

Clock Synchronization: Physical clock, Logical Clocks, Election Algorithms

3.2

Distributed Mutual Exclusion, Requirements of Mutual Exclusion Algorithms and

Performance measures.

Non- token Based Algorithms: Lamport, Ricart

Algorithms; Token-based Algorithms: Suzuki-

-based Algorithm; and Comparative Performance Analysis.

3.3

Deadlock: Introduction, Deadlock Detection: Centralized approach, Chandy -

Misra_Hass Algorithm.

Resource and Process Management

4.1

Desirable Features of Global Scheduling algorithm, Task assignment approach,

Load balancing approach and load sharing approach.

4.2

Introduction to Process Management, Process Migration, Code Migration.

Replication, Consistency and Fault Tolerance

5.1

Distributed Shared Memory: Architecture, design issues.

5.2

Introduction to replication and consistency, Data-Centric and Client-Centric

Consistency Models, Replica Management.

5.3

Fault Tolerance: Introduction, Process resilience, Recovery.

Distributed File Systems

6.1

Introduction and features of DFS, File models, File Accessing models, File-

Caching Schemes, File Replication, Case Study: Network File System (NFS).

6.2

Designing Distributed Systems: Google Case Study.

Textbooks:

Andrew S. Tanenbaum and Maarten Van Steen, Distributed Systems: Principles and

Paradigms, 2nd edition, Pearson Education.

Mukesh Singhal, Niranjan G. Shivaratri, "Advanced concepts in operating systems:

Distributed, Database and multiprocessor operating systems", MC Graw Hill education.

Pradeep K.Sinha, "Distributed Operating System-Concepts and design", PHI.

References:

         

Wesley, 2004

George Coulouris, Jean Dollimore, Tim Kindberg, "Distributed Systems: Concepts and

Design", 4th Edition, Pearson Education, 2005.

Useful Links

https://nptel.ac.in/courses/106106107

https://nptel.ac.in/courses/106106168

http://csis.pace.edu/~marchese/CS865/Lectures/Chap7/Chapter7fin.htm

https://nptel.ac.in/courses/106104182

Assessment:

Internal Assessment:

Assessment consists of two class tests of 20 marks each. The first-class test is to be conducted

when approx. 40% syllabus is completed and the second-class test when an additional 40%

syllabus is completed. The duration of each test shall be one hour.

End Semester Theory Examination:

The question paper will comprise a total of six questions.

All question carries equal marks

Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3

then part (b) will be from any module other than module 3)

Only four questions need to be solved.

In question paper weightage of each module will be proportional to the number of

respective lecture hours as mentioned in the syllabus.

Course Code:

Course Title

Credit

CSDC8011

Deep Learning

Prerequisite: Basic mathematics and Statistical concepts, Linear algebra, Machine

Learning

Course Objectives:

To learn the fundamentals of Neural Network.

To gain an in-depth understanding of training Deep Neural Networks.

To acquire knowledge of advanced concepts of Convolution Neural Networks,

Autoencoders and Recurrent Neural Networks.

Students should be familiar with the recent trends in Deep Learning.

Course Outcomes:

Gain basic knowledge of Neural Networks.

Acquire in depth understanding of training Deep Neural Networks.

Design appropriate DNN model for supervised, unsupervised and sequence learning

applications.

Gain familiarity with recent trends and applications of Deep Learning.

Modul

Content

39Hrs

Fundamentals of Neural Network

1.1

Biological neuron, Mc-Culloch Pitts Neuron, Perceptron, Perceptron

Learning, Delta learning, Multilayer Perceptron: Linearly separable,

linearly non-separable classes

1.2

Deep Networks: Fundamentals, Brief History, Three Classes of Deep

Learning Basic Terminologies of Deep Learning

Training, Optimization and Regularization of Deep Neural

Network

2.1

Training Feedforward DNN

Multi Layered Feed Forward Neural Network, Learning Factors,

Activation functions: Tanh, Logistic, Linear, Softmax, ReLU, Leaky

ReLU, Loss functions: Squared Error loss, Cross Entropy, Choosing

output function and loss function

2.2

Optimization

Learning with backpropagation, Learning Parameters: Gradient

Descent (GD), Stochastic and Mini Batch GD, Momentum Based GD,

Nesterov Accelerated GD, AdaGrad, Adam, RMSProp

2.3

Regularization

Overview of Overfitting, Types of biases, Bias Variance Tradeoff

Regularization Methods: L1, L2 regularization, Parameter sharing,

Dropout, Weight Decay, Batch normalization, Early stopping, Data

Augmentation, Adding noise to input and output

Autoencoders: Unsupervised Learning

3.1

Introduction, Linear Autoencoder, Undercomplete Autoencoder,

Overcomplete Autoencoders, Regularization in Autoencoders

3.2

Denoising Autoencoders, Sparse Autoencoders, Contractive

Autoencoders

3.3

Application of Autoencoders: Image Compression

Convolutional Neural Networks (CNN): Supervised Learning

4.1

Convolution operation, Padding, Stride, Relation between input, output

and filter size, CNN architecture: Convolution layer, Pooling Layer,

Weight Sharing in CNN, Fully Connected NN vs CNN, Variants of

basic Convolution function

4.2

Modern Deep Learning Architectures:

LeNET: Architecture, AlexNET: Architecture

Recurrent Neural Networks (RNN)

5.1

Sequence Learning Problem, Unfolding Computational graphs,

Recurrent Neural Network, Bidirectional RNN, Backpropagation

Through Time (BTT), Vanishing and Exploding Gradients, Truncated

BTT

5.2

Long Short Term Memory: Selective Read, Selective write, Selective

Forget, Gated Recurrent Unit

Recent Trends and Applications

6.1

Generative Adversarial Network (GAN): Architecture

6.2

Applications: Image Generation, DeepFake

Textbooks:

MIT Press Ltd, 2016

Publishers Inc.

Satish Kumar "Neural Networks A Classroom Approach" Tata McGraw-Hill.

 Jaico Publishing House



References:

Buduma, N. and Locascio, N., -generation

machine intelligence algorithms" 2017. O'Reilly Media, Inc.".





Simon Haykin, Neural Network- A Comprehensive Foundation- Prentice Hall

International, Inc

S.N.Sivanandam and S.N.Deepa, Principles of soft computing-Wiley India

Assessment:

Internal Assessment:

The assessment consists of two class tests of 20 marks each. The first class test is to be

conducted when approx. 40% syllabus is completed and second class test when additional 40%

syllabus is completed. Duration of each test shall be one hour.

End Semester Theory Examination:

Question paper will comprise a total of six questions.

All questions carry equal marks.

Question 1 and question 6 will have questions from all modules. Remaining 4 questions

will be based on the remaining 4 modules.

Only four questions need to be solved.

In question paper weightage of each module will be proportional to the number of

respective lecture hours as mentioned in the syllabus.

Useful Links

https://nptel.ac. https://deeplearning.cs.cmu.edu/S21/index.html

http://www.cse.iitm.ac.in/~miteshk/CS6910.html

https://nptel.ac.in/courses/106/106/106106184/

https://www.deeplearningbook.org/

Course Code:

Course Title

Credit

CSDC8012

Digital Forensics

Prerequisite: Computer Network, Cryptography and System Security

Course Objectives:

To discuss the need and process of digital forensics and Incident Response Methodology.

To explore the procedures for identification, preservation, and acquisition of digital

evidence.

To explore techniques and tools used in digital forensics for Operating system and malware

investigation .

To explore techniques and tools used for Mobile forensics and browser, email forensics

Course Outcomes:

Discuss the phases of Digital Forensics and methodology to handle the computer security

incident.

Describe the process of collection, analysis and recovery of the digital evidence.

Explore various tools to analyze malwares and acquired images of RAM/hard drive.

Acquire adequate perspectives of digital forensic investigation in mobile devices

Analyze the source and content authentication of emails and browsers.

Produce unambiguous investigation reports which offer valid conclusions.

Module

Content

Hrs

Introduction to Digital Forensics

1.1

Digital ForensicsDefination, Digital Forensics Goals, Digital

Forensics Categories - Computer Forensics, Mobile Forensics,

Network Forensics, Database Forensics

1.2

Introduction to Incident - Computer Security Incident, Goals of

Incident Response, CSIRT, Incident Response Methodology, Phase

after detection of an incident

Digital Evidence, Forensics Duplication and Digital Evidence

Acquisition

2.1

Digital evidence, Types of Digital Evidence, Challenges in acquiring

Digital evidence, Admissibility of evidence, Challenges in evidence

handling, Chain of Custody

2.2

Digital Forensics Examination Process - Seizure, Acquisition,

Analysis, Reporting. Necessity of forensic duplication, Forensic

image formats, Forensic duplication techniques,.

2.3

Acquiring Digital Evidence - Forensic Image File Format, Acquiring

Volatile Memory (Live Acquisition), Acquiring Nonvolatile Memory

(Static Acquisition), Hard Drive Imaging Risks and Challenges,

Network Acquisition

Forensics Investigation

3.1

Analyzing Hard Drive Forensic Images, Analyzing RAM Forensic

Image, Investigating Routers

3.2

Malware Analysis - Malware, Viruses, Worms, Essential skills and

tools for Malware Analysis, List of Malware Analysis Tools and

Techniques

Windows and Unix Forensics Investigation

4.1

Investigating Windows Systems - File Recovery, Windows Recycle

Bin Forensics, Data Carving, Windows Registry Analysis, USB

Device Forensics, File Format Identification, Windows Features

Forensics Analysis, Windows 10 Forensics, Cortana Forensics

4.2

Investigating Unix Systems - Reviewing Pertinent Logs, Performing

Keyword Searches, Reviewing Relevant Files, Identifying

Unauthorized User Accounts or Groups, Identifying Rogue

Processes, Checking for Unauthorized Access Points, Analyzing

Trust Relationships

Mobile Forensics

5.1

Android Forensics, Mobile Device Forensic Investigation - Storage

location, Acquisition methods, Data Analysis

5.2

GPS forensics - GPS Evidentiary data, GPS Exchange Format

(GPX), GPX Files, Extraction of Waypoints and TrackPoints,

Display the Tracks on a Map.

5.3

SIM Cards Forensics - The Subscriber Identification Module (SIM),

SIM Architecture, Security, Evidence Extraction.

Browser, Email Forensic & Forensic Investigation Reporting

6.1

Web Browser Forensics, Google chrome, Other web browser

investigation

Email forensics - Sender Policy Framework (SPF), Domain Key

Identified Mail (DKIM), Domain based Message Authentication

Reporting and Confirmation (DMARC)

6.2

Investigative Report Template, Layout of an Investigative Report,

Guidelines for Writing a Report

Textbooks:

         

McGrawHill, 2006

Digital Forensics Basics A Practical Guide Using Windows OS  Nihad A. Hassan,

APress Publication, 2019

Xiaodong Lin     -  

Springer Nature, 2018

Suggested MOOC Course Links



https://nptel.ac.in/courses/106/105/106105217/



https://onlinecourses.swayam2.ac.in/cec20_lb06/preview

Course on Cyber Incident Response

https://www.coursera.org/learn/incident-response



https://www.coursera.org/learn/ibm-penetration-testing-incident-response-forensics

Assessment:

Internal Assessment:

Assessment consists of two class tests of 20 marks each. The first class test is to be conducted

when approx. 40% syllabus is completed and second class test when additional 40% syllabus is

completed. Duration of each test shall be one hour.

End Semester Theory Examination:

Question paper will comprise a total of six questions.

All question carries equal marks

Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3

then part (b) will be from any module other than module 3)

Only Four questions need to be solved.

In question paper weightage of each module will be proportional to the number of

respective lecture hours as mention in the syllabus.

Course Code

Course Name

Credit

CSDC8013

Applied Data Science

Prerequisite: Machine Learning, Data Structures & Algorithms

Course Objectives:

To introduce students to the basic concepts of data science.

To acquire an in-depth understanding of data exploration and data visualization.

To be familiar with various anomaly detection techniques.

To understand the data science techniques for different applications.

Course Outcomes:

To gain fundamental knowledge of the data science process.

To apply data exploration and visualization techniques.

To apply anomaly detection techniques.

To gain an in-depth understanding of time-series forecasting.

Apply different methodologies and evaluation strategies.

To apply data science techniques to real world applications.

Module

Detailed Content

Hours

Introduction to Data Science

1.1

Introduction to Data Science, Data Science Process

1.2

Motivation to use Data Science Techniques: Volume, Dimensions and

Complexity, Data Science Tasks and Examples

1.3

Overview of Data Preparation, Modeling, Difference between data

science and data analytics

Data Exploration

2.1

Types of data, Properties of data

Descriptive Statistics:

Univariate Exploration: Measure of Central Tendency, Measure of

Spread, Symmetry, Skewness: Karl Pearson Coefficient of skewness,



Multivariate Exploration: Central Data Point, Correlation, Different

forms of correlation, Karl Pearson Correlation Coefficient for bivariate

distribution

2.2

Inferential Statistics:

Overview of Various forms of distributions: Normal, Poisson, Test

Hypothesis, Central limit theorem, Confidence Interval, Z-test, t-test,

Type-I, Type-II Errors, ANOVA

Methodology and Data Visualization

3.1

Methodology: Overview of model building, Cross Validation, K-

fold cross validation, leave-1 out, Bootstrapping

3.2

Data Visualization

Univariate Visualization: Histogram, Quartile, Distribution Chart

Multivariate Visualization: Scatter Plot, Scatter Matrix, Bubble chart,

Density Chart

Roadmap for Data Exploration

3.3

Self-Learning Topics: Visualizing high dimensional data: Parallel

chart, Deviation chart, Andrews Curves.

Anomaly Detection

4.1

Outliers, Causes of Outliers, Anomaly detection techniques, Outlier

Detection using Statistics

4.2

Outlier Detection using Distance based method, Outlier detection using

density-based methods, SMOTE

Time Series Forecasting

5.1

Taxonomy of Time Series Forecasting methods, Time Series

Decomposition

5.2

Smoothening Methods: Average method, Moving Average smoothing,

Time series analysis using linear regression, ARIMA Model,

Performance Evaluation: Mean Absolute Error, Root Mean Square

Error, Mean Absolute Percentage Error, Mean Absolute Scaled Error

5.3

Self-Learning Topics: Evaluation parameters for Classification,

regression and clustering.

Applications of Data Science

Predictive Modeling: House price prediction, Fraud Detection

Clustering: Customer Segmentation

Time series forecasting: Weather Forecasting

Recommendation engines: Product recommendation

Textbooks:



Publishers.

Steven 





Publications, New Delhi.

References:









Sons, New Delhi.



Useful Links

https://onlinecourses.nptel.ac.in/noc22_cs32/preview

https://onlinecourses.nptel.ac.in/noc21_cs69/preview

Assessment:

Internal Assessment:

Assessment consists of two class tests of 20 marks each. The first-class test is to be conducted

when approx. 40% syllabus is completed and second class test when additional40% syllabus is

completed. Duration of each test shall be one hour.

End Semester Theory Examination:

Question paper will comprise a total of six questions.

All questions carry equal marks.

Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3

then part (b) will be from any module other than module 3).

Only Four questions need to be solved.

Course Code:

Course Title

Credit

CSDC8021

Optimization in Machine Learning

Prerequisite: Engineering Mathematics, Algorithms and data structures

Course Objectives:

1. Understand, analyze and apply existing derivative based optimization algorithms

2. Analyze and apply stochastic methods in optimization

3. Analyze convex optimization for machine learning problems

4. Understand real life problems and apply evolutionary methods to optimize them

Course Outcomes:

1. To understand foundational optimization ideas including gradient descent, stochastic gradient

methods

2. To apply convex optimization algorithm

3. To analyze and demonstrate several population methods in Evolutionary Computation

4.Toapply advanced evolutionary algorithms such as particle swarm and ant colony

optimization

Module

Content

Hrs

Introduction and Background to Optimization Theory

1.1

Basic Ingredients of Optimization Problems, Optimization

Problem Classifications, Optima Types, Optimization Method

Classes, Overview of Unconstrained and Constrained

Optimization, Basics of convex optimization

Derivative based Optimization

2.1

2.2

2.3

The Basics of Optimization (univariate, bivariate and

multivariate optimization), Convex Objective Functions

First-Order optimization Methods : Gradient Descent,

Conjugate Gradient, Momentum, Nesterov Momentum,

Adagrad, RMSProp, learning rate optimization

Second order optimization: Newton method

Stochastic Methods

Noisy Descent, Mesh Adaptive Direct Search, Cross-Entropy

Method, Natural Evolution Strategies, Covariance Matrix

Adaptation

Convex Optimization

Optimization problems, Convex optimization, Linear

optimization problems, Quadratic optimization problems,

Geometric programming, Overview of Generalized inequality

constraints and Vector optimization

Evolutionary Methods

5.1

5.2

Introduction to Evolutionary Computation: Generic

Evolutionary Algorithm, Representation: The Chromosome,

Initial Population, Fitness Function, Selection: Selective

Pressure, Random Selection, Proportional

Selection,Tournament Selection, Rank-Based Selection,

Elitism and Evolutionary Computation versus Classical

Optimization, Stopping conditions

Canonical Genetic Algorithm, Binary Representations of

Crossover and Mutation: Binary Representations, Control

Parameters

Advance Evolutionary Methods

6.1

Basic Particle Swarm Optimization, Global Best PSO, Local

Best PSO, g-best versus l-best PSO, Velocity Components,

Geometric Illustration, Algorithm Aspects, Social Network

Structures

6.2

Ant Colony Optimization Meta-Heuristic, Foraging Behavior

of Ants, Stigmergy and Artificial Pheromone, Simple Ant

Colony Optimization, Ant System, Ant Colony System

Textbooks:

Mykel J. Kochenderfer, Tim A.Wheeler, Algorithms for Optimization, MIT Press (2019)

Andries P Engelbrecht, Computational Intelligence-An Introduction, Second-Edition,

Wiley publication

Charu C. Aggarwal, Linear Algebra and Optimization for Machine Learning, , Springer

,2020.

References:

SuvritSra, Sebastian Nowozin, Stephen J. Wright, Optimization for Machine Learning,

The MIT Press

Xin-She Yang Middlesex ,Optimization techniques and applications with examples,

Wiley

A.E. Eiben, J. E. Smith, Introduction to Evolutionary Computing, Springer

Useful Links

Convex optimization (NPTEL)

Constrained and Unconstrained optimization (NPTEL)

Machine-learning-model-performance (Coursera)

Deep-neural-network optimization (Coursera)

Assessment:

Internal Assessment:

Assessment consists of two class tests of 20 marks each. The first class test is to be conducted

when approx. 40% syllabus is completed and second class test when additional 40% syllabus is

completed. Duration of each test shall be one hour.

End Semester Theory Examination:

Question paper will comprise a total of six questions.

All questions carry equal marks

Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3

then part (b) will be from any module other than module 3)

Only Four questions need to be solved.

In question paper weightage of each module will be proportional to the number of

respective lecture hours as mentioned in the syllabus.

Course Code:

Course Title

Credit

CSDC8022

High Performance Computing

Prerequisite: Computer Architecture, Operating System, Cloud Computing

Course Objectives: The objective of the course is to

Introduce the fundamental concepts of high-performance computing (HPC) architecture

and parallel computing.

Provide foundations for developing, analyzing, and implementing parallel algorithms

using parallelization paradigms like MPI, OpenMP, OpenCL, and CUDA.

Introduce range of activities associated with HPC in Cloud

Course Outcomes: After learning the course, the students will be able to:

Understand parallel and pipeline processing approaches

Design a parallel algorithm to solve computational problems and identify issues in parallel

programming.

Analyze the performance of parallel computing systems for clusters in terms of execution

time, total parallel overhead, speedup.

Develop efficient and high-performance parallel algorithms using OpenMP and message

passing paradigm

Develop high-performance parallel programming using OpenCL and CUDA framework

Perform the range of activities associated with High Performance Computing in Cloud

Computing

Module

Content

Hrs

Introduction to Parallel Computing

1.1

1.2

1.3

1.4

Parallelism (What, Why, Applications), Levels of parallelism(instruction,

transaction, task, thread, memory, function)

Classification Models: Architectural Schemes(  



Memory Access: Distributed Memory, Shared Memory, Hybrid Distributed

Shared Memory

Parallel Architecture: Pipeline Architecture: Arithmetic pipelines, Floating

Point, Array Processor

Parallel Programming Platform and Algorithm Design

2.1

2.2

Parallel Programming Platform: Physical Organization of Parallel

Platforms, Communication Costs in Parallel Machines

Algorithm Design: Preliminaries, Decomposition Techniques,

Characteristics of Tasks and Interactions, Mapping Techniques for Load

Balancing, Methods for Containing Interaction Overheads, Parallel

Algorithm Models.

Performance Measures

Performance Measures: Speedup, execution time, efficiency, cost,

scalability, Effect of granularity on performance, Scalability of Parallel

    

Karp Flatt Metric.

HPC Programming: OpenMP and MPI

4.1

4.2

4.3

4.4

4.5

4.6

4.7

HPC Programming: OpenMP

Introduction: Threads, Share memory Architecture, Multi-core processors

and Hyperthreading, Fork and join model.

OpenMP directives: #pragma omp parallel, Hello world with openMP,

#pragma omp for, #pragma omp for schedule.Serial vs Parallel PI program.

Synchronisation: Introduction, Private vs Shared variables.

Critical section, #pragma omp critical, #pragma omp atomic, #pragma omp

barrier, #pragma omp reduction

HPC Programming: MPI

Introduction: Processes, Multiprocessor programming model, Distributed

system programming model, Inter-process communication using message

passing: Asynchronous and Synchronous

MPI Programming: Hello world problem, mpi_initMPI_sendMPI_Recv,

Synchronisation: MPI_Barrier

Hybrid (MPI + OpenMP) programming, Hardware requirement, Threads

inside Processes, Hybrid Matrix multiplication

Message passing vs Share memory communication: Advantages and

disadvantage

Parallel programming using accelerators

An Overview of GPGPUs, Introduction to CUDA, Introduction to

Heterogeneous Computing using OpenCL, An Overview of OpenCL API,

Heterogeneous Programming in OpenCL.

High Performance Computing in the Cloud

Virtualization and Containerization, Parallel Computing Frameworks,

Scaling, HPC in the Cloud Use Cases.

Textbooks:



 2nd edition, Addison Wesley, 2003.

    Developer's Guide to Parallel

2012.



  

cGraw Hill, Second Edition, 2010.

 High Performance Computing

for 

References:



Education, 2008.

Kai Hwang ,Zhiwei,     

-Hill Education, 1998.

Laurence T. Yang, Minyi Guo, -Performance Computing: Paradigm and



Useful Links

https://nptel.ac.in/courses/112105293

https://archive.nptel.ac.in/courses/128/106/128106014/

Assessment:

Internal Assessment:

Assessment consists of two class tests of 20 marks each. The first-class test is to be conducted

when approx. 40% syllabus is completed and second-class test when additional 40% syllabus is

completed. Duration of each test shall be one hour.

End Semester Theory Examination:

Question paper will comprise of total six questions.

All question carries equal marks

Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3

then part (b) will be from any module other than module 3)

Only four questions need to be solved.

In question paper weightage of each module will be proportional to number of respective

lecture hours as mention in the syllabus.

Course Code

Course Name

Credit

CSDC8023

Social Media Analytics

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

Course Objectives: The course aims:

Familiarize the learners with the concept of social media.

Familiarize the learners with the concept of social media analytics and understand

its significance.

Enable the learners to develop skills required for analyzing the effectiveness of

social media.

Familiarize the learners with different tools of social media analytics.

Familiarize the learner with different visualization techniques for Social media

analytics.

Examine the ethical and legal implications of leveraging social media data.

Course Outcomes:

Understand the concept of Social media

Understand the concept of social media Analytics and its significance.

Learners will be able to analyze the effectiveness of social media

Learners will be able to use different Social media analytics tools effectively and

efficiently.

Learners will be able to use different effective Visualization techniques to represent

social media analytics.

Acquire the fundamental perspectives and hands-on skills needed to work with

social media data.

Module

Detailed Content

Hours

Social Media Analytics: An Overview

Core Characteristics of Social Media, Types of Social Media, Social

media landscape, Need for Social Media Analytics (SMA), SMA in

small & large organizations.

Purpose of Social Media Analytics, Social Media vs. Traditional

Business Analytics, Seven Layers of Social Media Analytics, Types

of Social Media Analytics, Social Media Analytics Cycle, Challenges

to Social Media Analytics, Social Media Analytics Tools

Social Network Structure, Measures & Visualization

Basics of Social Network Structure - Nodes, Edges & Tie

Describing the Networks Measures - Degree Distribution, Density,

Connectivity, Centralization, Tie Strength & Trust

Network Visualization - Graph Layout, Visualizing Network

features, Scale Issues.

Social Media Network Analytics - Common Network Terms,

Common Social Media Network Types, Types of Networks,

Common Network Terminologies, Network Analytics Tools.

Social Media Text, Action & Hyperlink Analytics

Social Media Text Analytics - Types of Social Media Text, Purpose

of Text Analytics, Steps in Text Analytics, Social Media Text

Analysis Tools

Social Media Action Analytics - What Is Actions Analytics?

Common Social Media Actions, Actions Analytics Tools

Social Media Hyperlink Analytics - Types of Hyperlinks, Types of

Hyperlink Analytics, Hyperlink Analytics Tools

Social Media Location & Search Engine Analytics

Location Analytics - Sources of Location Data, Categories of

Location Analytics, Location Analytics and Privacy Concerns,

Location Analytics Tools

Search Engine Analytics - Types of Search Engines, Search Engine

Analytics, Search Engine Analytics Tools

Social Information Filtering

Social Information Filtering - Social Sharing and filtering ,

Automated Recommendation systems, Traditional Vs social

Recommendation Systems

Understanding Social Media and Business Alignment, Social Media

KPI, Formulating a Social Media Strategy, Managing Social Media

Risks

Social Media Analytics Applications and Privacy

Social media in public sector - Analyzing public sector social media,

analyzing individual users, case study.

Business use of Social Media - Measuring success, Interaction and

monitoring, case study.

Privacy - Privacy policies, data ownership and maintaining privacy

online.

Textbooks:

Seven Layers of Social Media Analytics_ Mining Business Insights from Social Media

Text, Actions, Networks, Hyperlinks, Apps, Search Engine, and Location Data, Gohar

F. Khan,(ISBN-10: 1507823207).

Analyzing the Social Web 1st Edition by Jennifer Golbeck

Mining the Social Web_ Analyzing Data from Facebook, Twitter, LinkedIn, and

Other Social Media Sites

Charu Aggarwal (ed.), Social Network Data Analytics, Springer, 2011

References:

Social Media Analytics [2015], Techniques and Insights for Extracting Business Value

Out of Social Media, Matthew Ganis, AvinashKohirkar, IBM Press

Social Media Analytics Strategy_ Using Data to Optimize Business Performance, Alex

Gonçalves, APress Business Team

Social Media Data Mining and Analytics, Szabo, G., G. Polatkan, O. Boykin & A.

Chalkiopoulus (2019), Wiley, ISBN 978-1-118-82485-6

Useful Links

https://cse.iitkgp.ac.in/~pawang/courses/SC16.html

https://onlinecourses.nptel.ac.in/noc20_cs78/preview

https://nptel.ac.in/courses/106106146

https://7layersanalytics.com/

Assessment:

Internal Assessment:

Assessment consists of two class tests of 20 marks each. The first-class test is to be

conducted when approx. 40% syllabus is completed and second class test when

additional40% syllabus is completed. Duration of each test shall be one hour.

End Semester Theory Examination:

Question paper will consist of 6 questions, each carrying 20 marks.

The students need to solve a total of 4 questions.

Question No.1 will be compulsory and based on the entire syllabus.

Remaining question (Q.2 to Q.6) will be selected from all the modules.

Lab Code

Lab Name

Credit

CSL801

Distributed Computing Lab

Prerequisite: Computer Networks and Operating Systems.

Lab Objectives:

To understand basic underlying concepts of forming distributed systems.

To learn the concept of clock Synchronization

To learn Election Algorithm.

To explore mutual exclusion algorithms and deadlock handling in the distributed system

To study resource allocation and management.

To understand the Distributed File System

Lab Outcomes: At the end of the course, the students will be able to

Develop test and debug usingMessage-Oriented Communication or RPC/RMI based

client-server programs.

Implement techniques for clock synchronization.

Implement techniques for Election Algorithms.

Demonstrate mutual exclusion algorithms and deadlock handling.

Implement techniques of resource and process management.

Describe the concepts of distributed File Systems with some case studies.

Suggested List of Experiments

Sr. No.

Title of Experiment

Inter-process communication

Client/Server using RPC/RMI

Group Communication

Clock Synchronization algorithms

Election Algorithm.

Mutual Exclusion Algorithm

Deadlock Management in Distributed System

Load Balancing

Distributed shared Memory

Distributed File System (AFS/CODA)

Case Study: CORBA

Case Study: Android Stack

Term Work:

Term work should consist of 10 experiments.

Journal must include at least 2 assignments on content of theory and practical of CSC801 and

CSL801(Distributed Computing)

The final certification and acceptance of term work ensure satisfactory performance of

laboratory work and minimum passing marks in term work.

Total 25 Marks (Experiments: 15-marks, Attendance Theory & Practical: 05-marks,

Assignments: 05-marks)

Oral and Practical exam

Based on the entire syllabus of CSC801: Distributed Computing and CSL801: Distributed

Computing Lab

Lab Code

Lab Name

Credit

CSDL8021

Deep Learning Lab

Prerequisite: Python Programming, Engineering Mathematics

Lab Objectives:

To implement basic neural network models for simulating logic gates.

To implement various training algorithms for feedforward neural networks.

To design deep learning models for supervised, unsupervised and sequence learning.

Lab Outcomes: At the end of the course, the students will be able to

Implement basic neural network models to learn logic functions.

Design and train feedforward neural networks using various learning algorithms.

Build and train deep learning models such as Autoencoders, CNNs, RNN, LSTM etc.

Suggested List of Experiments

Based on Module 1 (Any two) using Virtual Lab

1. Implement Mc-Culloch Pitts model for binary logic functions.

2. Implement Perceptron algorithm to simulate any logic gate.

3. Implement Multilayer Perceptron algorithm to simulate XOR gate.

4. To explore python libraries for deep learning e.g. Theano, TensorFlow etc.

Module 2 (Any Two)

5. Apply any of the following learning algorithms to learn the parameters of the

supervised single layer feed forward neural network.

a. Stochastic Gradient Descent

b. Mini Batch Gradient Descent

c. Momentum GD

d. Nestorev GD

e. Adagrad GD

f. Adam Learning GD

6. Implement a backpropagation algorithm to train a DNN with at least 2 hidden

layers.

7. Design and implement a fully connected deep neural network with at least 2

hidden layers for a classification application. Use appropriate Learning Algorithm,

output function and loss function.

Module 3 (Any One)

8. Design the architecture and implement the autoencoder model for Image

Compression.

9. Design the architecture and implement the autoencoder model for Image

denoising.

Module 4 (Any One)

10. Design and implement a CNN model for digit recognition application.

11. Design and implement a CNN model for image classification.

Module 5 (Any One)

12. Design and implement LSTM for Sentiment Analysis.

13. Design and implement GRU for classification on text data.

14. Design and implement RNN for classification of temporal data.

Term Work:

Term work should consist of 8 experiments.

The final certification and acceptance of term work ensures satisfactory performance of

laboratory work and minimum passing marks in term work.

The final certification and acceptance of term work ensures satisfactory performance of

laboratory work and minimum passing marks in term work. Total 25 Marks (Experiments:

15-marks, Attendance Theory & Practical: 05-marks, Assignment: 05-marks)

Practical and Oral exam

Based on the entire syllabus of CSDC8011: Deep Learning and CSDL8011: Deep Learning

Lab

Lab Code

Lab Name

Credit

CSDL8022

Digital Forensics Lab

Prerequisite: Computer Network, Cryptography and System Security

Lab Objectives:

To demonstrate the procedures for identification, preservation, and acquisition of digital

evidence.

To demonstrate techniques and tools used in digital forensics for operating systems and

malware investigation.

To demonstrate tools formobile forensics and browser, email forensics

To explore scenario based crime forensics investigations.

Lab Outcomes: At the end of the course, the students will be able to

Explore various forensics tools and use them to acquire, duplicate and analyze data and

recover deleted data.

Implement penetration testing using forensics tools.

Explore various forensics tools and use them to acquire and analyze live and static data.

Verification of source and content authentication of emails and browsers.

Demonstrate Timeline Report Analysis using forensics tools.

Discuss real time crime forensics investigations scenarios.

Suggested List of Experiments

Sr. No.

Title of Experiment

Analysis of forensic images using open source tools.

 FTK Imager

 Autopsy

Explore forensics tools in kali linux for acquiring, analyzing and duplicating data.

 dd

 dcfldd

Performing penetration testing using Metasploit - kali Linux.

Performing RAM Forensic to analyze memory images to find traces of an attack.

 Capturing RAM Using the DumpIt Tool

 Volatility tool

Network forensics using Network Miner.

Windows Recycle Bin Forensics

Data Carving using open source tools

 Foremost

 Scalpel

 Jpegcarver

USB Device Forensics using

 USBDeview

 USB Detective

Web Browser Forensics using DB Browser for SQLite

Generate a Timeline Report Using Autopsy

Email Analysis

Case Study

Term Work:

Term work should consist of 7 experiments covering all the modules and one case study.

Journal must include at least 2 assignments on content of theory and practical

The final certification and acceptance of term work ensures satisfactory performance of

laboratory work and minimum passing marks in term work.

Total 25 Marks (Experiments & Case Study : 15-marks, Attendance Theory & Practical: 05-

marks, Assignments: 05-marks)

Oral & Practical exam

Based on the entire syllabus of CSDC8012- Digital Forensics and CSDL8012- Digital

Forensics Lab

Lab Code

Lab Name

Credit

CSL8023

Applied Data Science Lab

Prerequisite: Engineering Mathematics, Machine Learning, Programming fundamentals

Lab Objectives:

To explore various stages in the data science lifecycle.

To understand data preparation, exploration and visualization techniques.

To model and evaluate different supervised/unsupervised learning techniques.

Lab Outcomes: At the end of the course, the students will be able to

Apply various stages of the data science lifecycle for the selected case study.

Demonstrate data preparation, exploration and visualization techniques.

Implement and evaluate different supervised and unsupervised techniques.

Suggested List of Experiments

(Select a case study and perform the experiments 1 to 8.).

Star (*) marked experiments are compulsory.

Name of the Experiment

1. Explore the descriptive and inferential statistics on the given dataset.

2. Apply data cleaning techniques (e.g. Data Imputation).

3. Explore data visualization techniques.

4. Implement and explore performance evaluation metrics for Data Models

(Supervised/Unsupervised Learning)

5. Use SMOTE technique to generate synthetic data.(to solve the problem of class imbalance)

6. Outlier detection using distance based/density based method.

7. Implement time series forecasting.

Illustrate data science lifecycle for selected case study. (Prepare case study document for the

selected case study)

Suggested Case Studies:

1. Customer Segmentation

2. Fraud Detection

3. House Price prediction

4. Product Recommendation

5. Stock price prediction

6. Weather prediction

Suggested Assignment List

Assignments can be given on self learning topics or data deployment tools.

Term Work:

Term work should consist of 8 experiments.

The final certification and acceptance of term work ensures satisfactory performance of

laboratory work and minimum passing marks in term work.

The final certification and acceptance of term work ensures satisfactory performance of

laboratory work and minimum passing marks in term work. Total 25 Marks (Experiments: 15-

marks, Attendance Theory & Practical: 05-marks, Assignment: 05-marks)

Practical and Oral exam

Based on the entire syllabus of CSDC 8013: Applied Data Science and CSDL 8013: Applied

Data Science Lab

Lab Code

Lab Name

Credit

CSDL8021

Optimization in Machine Learning

Prerequisite: Algorithms and data structures

Lab Objectives:

To apply derivative based optimization techniques

To understand evolutionary optimization to a given machine learning problem.

To apply advanced evolutionary optimization

To design and analyze optimization problems for real world applications

Lab Outcomes: At the end of the course, the students will be able to

To implement derivative based optimization techniques

To implement evolutionary optimization

To implement advanced evolutionary optimization

To apply efficient optimization algorithm for real world applications

Suggested List of Experiments

Sr. No.

Title of Experiment

To implement Gradient Descent algorithm

To implement the Stochastic Gradient Descent algorithm

To implement Newton method

To apply Genetic Algorithm for real world problem

To compare and implement different selection mechanism using genetic algorithm

To implement various mutation and crossover mechanisms

To implement Particles Swarm optimization

To implement Ant colony optimization

Term Work:

Term work should consist of 6 experiments.

Journal must include at least 2 assignments on content of theory and practical of

Optimization in Machine Learning

The final certification and acceptance of term work ensures satisfactory performance of

laboratory work and minimum passing marks in term work.

Total 25 Marks (Experiments and assignments: 15-marks, Attendance Theory & Practical: 05-

marks, Case study /Mini project: 05-marks)

Practical and Oral exam

Based on the entire syllabus of CSDC8021: Optimization in Machine Learning and

CSDL8021: Optimization in Machine Learning

Lab Code

Lab Name

Credit

CSDL8022

High Performance Computing Lab

Prerequisite: C Programming

Lab Objectives: The objective of the course is to:

Enable students to build the logic to parallelize the programming task.

Give insight about performance of parallel computing systems.

Provide hands-on experience on parallel programming platforms/frameworks

Lab Outcomes: After learning the course, the students will be able to:

Perform Linux based commands on remote machine

Compare the performance of sequential algorithms with parallel algorithm in terms of

execution time, speedup and throughput.

Implement parallel program using OpenMP libraryand analyze its performance

Implement parallel program using MPIplatform and analyze its performance

Implement parallel program using OpenCL framework and analyze its performance

Implement parallel program using CUDA framework and analyze its performance

Suggested Experiments: Students are required to complete at least 8 experiments.

Star (*) marked experiments are compulsory.

Sr.

No.

Name of the Experiment

To analyse the Linux based computer systems using following commands:

a. top , b.ps , c. kill, d. cat /proc/cpuinfoe.vmstat

Hardware/Software Requirement: Linux Operating System

To setup SSH passwordless logins for two or more Linux based machines and execute

commands on a remote machine.

Hardware/Software Requirement: Linux Operating System,

Multi-core computer systems

Write a program in C to multiply two matrices of size 10000 x 10000 each and find it's

execution-time using "time" command. Try to run this program on two or more

machines having different configurations and compare execution-times obtained in

each run. Comment on which factors affect the performance of the program.

Hardware/Software Requirement: Linux Operating System, gcc compiler,Multi-core

computer systems

Write a "Hello World" program using OpenMP library also display number of threads

created during execution.

Hardware/Software Requirement: Linux Operating System, gcc compiler,Dual core

with HT or Quad-core or higher computer system.

Write a parallel program to calculate the value of PI/Area of Circle using OpenMP

library.

Hardware/Software Requirement: Linux Operating System, gcc compiler,Dual core

with HT or Quad-core or higher computer system.

Write a parallel program to multiply two matrices using openMP library and compare

the execution time with its serial version. Also change the number of threads using

omp_set_num_threads() function and analyse how thread count affects the execution

time.

Hardware/Software Requirement: Linux Operating System, gcc compiler,Dual core

with HT or Quad-core or higher computer system.

Install MPICH library and write a "Hello World" program for the same.

Hardware/Software Requirement: Linux Operating System, MPICH, Multi-processor

systems or MPI Cluster.

Write a parallel program to multiply two matrices using MPI library and compare the

execution-

Hardware/Software Requirement: Linux Operating System, MPICH, gcc, Multi-

processor systems, or MPI Cluster.

Install MPICH on two and more machines and create a MPI cluster. Execute MPI

programs on this cluster and check the performance.

Hardware/Software Requirement: Linux Operating System, MPICH, Multi-processor

systems or MPI Cluster.

10*

Implement a program to demonstrate balancing workload on MPI platform.

Hardware/Software Requirement: Linux Operating System, MPICH, Multi-processor

systems or MPI Cluster.

Implement a parallel program to demonstrate the cube of N number within a set range

using MPI/OpenMP/OpenCL/CUDA.

Hardware/Software Requirement: Linux Operating System, MPICH, Multi-processor

systems or MPI Cluster.

A CUDA-capable GPU,A supported version of Microsoft Windows,A supported version

of Microsoft Visual Studio, The NVIDIA CUDA Toolkit

Implement DFT computation of vector using OpenCL/CUDA/ Parallel Matlab

Hardware/Software Requirement: A CUDA-capable GPU,A supported version of

Microsoft Windows,A supported version of Microsoft Visual Studio, The NVIDIA

CUDA Toolkit

Implement Two Vector addition using OpenCL/CUDA/ Parallel Matlab

Hardware/Software Requirement: A CUDA-capable GPU, A supported version of

Microsoft Windows,A supported version of Microsoft Visual Studio, The NVIDIA

CUDA Toolkit

Implement even-odd/Bucket /Radix /Shell sort using OpenCL/CUDA/ Parallel Matlab

Hardware/Software Requirement: A CUDA-capable GPU, A supported version of

Microsoft Windows,A supported version of Microsoft Visual Studio, The NVIDIA

CUDA Toolkit

Term Work:

Term work should consist of 8 experiments.

Journal must include at least 2 assignments.

The final certification and acceptance of term work ensures that satisfactory performance of

laboratory work and minimum passing marks in term work.

Total 25 Marks (Experiments: 15-marks, Attendance Theory & Practical: 05-marks,

Assignments: 05-marks)

Practical and Oral Exam

Based on the entire syllabus of CSDC8022 : High Performance Computing and

CSDL8022 High Performance Computing Lab

Lab Code

Lab Name

Credit

CSDL8023

Social Media Analytics Lab

Prerequisite: Types of Graphs, Data Mining, Data Analytics

Lab Objectives:

To understand the fundamental concepts of social media networks.

To learn various social media analytics tools and evaluation matrices.

To collect and store social media data.

To analyze and visualize social media data

To design and develop social media analytics models.

To design and build a social media analytics application.

Lab Outcomes: The students will be able to

Understand characteristics and types of social media networks.

Use social media analytics tools for business

Collect, monitor , store and track social media data

Analyze and visualize social media data from multiple platforms

Design and develop content and structure based social media analytics models.

Design and implement social media analytics applications for business.

Suggested Experiments:

Sr. No.

Name of the Experiment

Study various -

i) Social Media platforms ( Facebook, twitter, YouTubeetc)

ii) Social Media analytics tools ( Facebook insights, google analytics net

lyticetc)

iii) Social Media Analytics techniques and engagement metrics (page level,

post level, member level)

iv) Applications of Social media analytics for business.

e.g. Google Analytics

https://marketingplatform.google.com/about/analytics/

https://netlytic.org/

Data Collection-Select the social media platforms of your choice (Twitter,

Facebook, LinkedIn, YouTube, Web blogs etc) ,connect to and capture social media

data for business ( scraping, crawling, parsing).

Data Cleaning and Storage- Preprocess, filter and store social media data for

business (Using Python, MongoDB, R, etc).

Exploratory Data Analysis and visualizationof Social Media Data for business.

Develop Content (text, emoticons, image, audio, video) based social media analytics

model for business.

(e.g. Content Based Analysis :Topic , Issue ,Trend, sentiment/opinion analysis,

audio, video, image analytics)

Develop Structure based social media analytics model for any business.

( e.g. Structure Based Models -community detection, influence analysis)

Develop a dashboard and reporting tool based on real time social media data.

Design the creative content for promotion of your business on social media

platform.

Analyze competitor activities using social media data.

Develop social media text analytics models for improving existing product/ service



Reference Books:

Python Social Media Analytics: Analyze and visualize data from Twitter, YouTube,

GitHub, and more Kindle Edition by Siddhartha Chatterjee , Michal Krystyanczuk

Learning Social Media Analytics with R,byRaghav Bali, Dipanjan Sarkar, Tushar

Sharma.

Jennifer Golbeck, Analyzing the social web, Morgan Kaufmann, 2013

Matthew A. Russell. Mining the Social Web: Data Mining Facebook, Twitter,

Linkedin, Google+, Github, and More, 2nd Edition, O'Reilly Media, 2013

Charu Aggarwal (ed.), Social Network Data Analytics, Springer, 2011

Term Work:

Term work should consist of 10 experiments.

Journal must include at least 2 assignments.

The final certification and acceptance of term work ensures satisfactory performance

of laboratory work and minimum passing marks in term work.

Total 25 Marks (Experiments: 15-marks, Attendance Theory & Practical: 05-marks,

Assignments: 05-marks)

Practical and Oral Exam

Based on the entire syllabus of CSDC8023: Social Media Analytics and CSDL80223: Social

Media Analytics Lab

Course Code

Course Name

Credit

CSP801

Major Project 2

Course Objectives::

The Project work facilitates the students to develop and prove Technical, Professional and

Ethical skills and knowledge gained during graduation program by applying them from

problem identification to successful completion of the project by implementing the solution.

Course Outcomes: Student will able to

Implement solutions for the selected problem by applying technical and

professional skills.

Analyze impact of solutions in societal and environmental context for sustainable

development.

Collaborate best practices along with effective use of modern tools.

Develop proficiency in oral and written communication with effective leadership

and teamwork.

Nurture professional and ethical behavior.

Gain expertise that helps in building lifelong learning experience.

Guidelines:

1. Internal guide has to keep track of the progress of the project and also has to maintain

attendance report. This progress report can be used for awarding term work marks.

2. Project Report Format:

At the end of semester, each group need to prepare a project report as per the guidelines

issued by the University of Mumbai. Report should be submitted in hardcopy. Also, each

group should submit softcopy of the report along with project documentation,

implementation code, required utilities, software and user Manuals.

A project report should preferably contain at least following details:

o Abstract

o Introduction

o Literature Survey/ Existing system

o Limitation Existing system or research gap

o Problem Statement and Objective

o Proposed System

o Analysis/Framework/ Algorithm

o Design details

o Methodology (your approach to solve the problem) Proposed System

o Experimental Set up

o Details of Database or details about input to systems or selected data

o Performance Evaluation Parameters (for Validation)

o Software and Hardware Set up

o Results and Discussion

o Conclusion and Future Work

o References

o Appendix  List of Publications or certificates

Desirable:

Students should be encouraged -

o to participate in various project competition.

o to write minimum one technical paper & publish in good journal.

o to participate in national / international conference.

3. Term Work:

Distribution of marks for term work shall be done based on following:

a. Weekly Log Report

b. Completeness of the project and Project Work Contribution

c. Project Report (Black Book) (both side print)

d. Term End Presentation (Internal)

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

the above aspects.

4. Oral & Practical:

Oral &Practical examination (Final Project Evaluation) of Project 2 should be conducted by

Internal and External examiners approved by University of Mumbai at the end of the

semester.

Suggested quality evaluation parameters are as following:

a. Relevance to the specialization / industrial trends

b. Modern tools used

c. Innovation

d. Quality of work and completeness of the project

e. Validation of results

f. Impact and business value

g. Quality of written and oral presentation

h. Individual as well as team work