Human Computer Interaction

[As per Choice Based Credit System (CBCS) scheme]
Department of Computer Science and Engineering
Instructor: Dr. Gokulakrishnan S
Course Code: 2CS4717
Hours / Week: 03 Hours
L-T-P-J: 3–0–0–0
Credits: 03
Total Hours: 39 Hours
Semester: VII
CIA Marks WhatsApp Group Course Exit Survey HCI Unit 1 & 2 Quiz – MCQs
Course Learning Objectives (CLOs)
CLO1. Demonstrate an understanding of guidelines, principles, and theories influencing human computer interaction.
CLO2. Recognize how a computer system may be modified to include human diversity.
CLO3. Design mock ups and carry out user and expert evaluation of interfaces.
CLO4. Use the information sources available, and be aware of the methodologies and technologies supporting advances in HCI.
Unit-wise Topics
UNIT — I (08 Hours)
HCI INTRODUCTION
The Human: I/O channels — Memory — Reasoning and problem solving; The computer: Devices — Memory — processing and networks; Historical evolution of HCI; Interaction: Models — frameworks — Ergonomics — styles — elements — interactivity — Paradigms.
📖 Textbook 1: Chapter 1 to 4
UNIT — II (09 Hours)
SOFTWARE PROCESS, MODELS AND THEORIES
HCI in software process — software life cycle — usability engineering — Prototyping in practice — design rationale. Cognitive models — Socio-Organizational issues and stakeholder requirements — Communication and collaboration models. Keystroke level model (KLM), GOMS, CASE STUDIES. Shneiderman's eight golden rules; Norman's seven principles; Norman's model of interaction; Nielsen's ten heuristics with example of use.
📖 Textbook 1: Chapter 6 to 12
UNIT — III (08 Hours)
GETTING STARTED WITH GAME DEVELOPMENT
Create Folders — Importing Textures and Meshes — Configuring Meshes — Planning and Configuring Textures — Building Sprites — Importing Audio — Create Prefabs — Scene Building — Lighting and Lightmapping — Building a Navigation Mesh.
📖 Textbook 2: Chapter 1 and 2
UNIT — IV (08 Hours)
EVENT HANDLING & PLAYER CONTROLLER
Event Handling — Notifications Manager — Send Message and Broadcast Message — Character Controllers and the First-Person Controller — Beginning the Universal First Person Controller — Handling Cash Collection — Life and Death: Getting Started.
📖 Textbook 2: Chapter 3 and 5
UNIT — V (06 Hours)
WEAPONS & ENEMIES
Object Orientation: Classes, Instances and Inheritance — Animations, Frames, and Prefabs — Cameras — Starting the Enemy Drone Prefab — Enemies, Intelligence, and Philosophical Zombies.
📖 Textbook 2: Chapter 6 and 7
Course Outcomes (COs)
CO1. Recognize and analyze basics of Human Computer Interface, ergonomics, and paradigm models.
L3
CO2. Outline navigational design, usability evaluation, software life cycle, and design rules.
L2
CO3. Relate cognitive and collaboration models, Norman's principles, and heuristics.
L3
CO4. Design mobile/web GUI interfaces with design tools and case studies.
L3
CO5. Implement conversational interfaces and apply them to real-world applications.
L3
Evaluation Components:
Component Details Marks
MSE-1 Mid-Semester Exam 30
Assignment-1 Unit 1 & 2 10
Class Test-1 Unit 3 & 4 10
Open Book Exam [OBE] Unit 5 10
SEE Semester End Examination 40
Total 100
Examinations
Mid Semester Test 1
📅 October 10th to 13th
Final SEE Examination
📅 Will announce the Date
Text Books
0
  • Alan Dix, Finlay, Gregory Abowd, Russell Beale — Human Computer Interaction (3rd Ed)
    Pearson Education, 2004
  • Pro Unity Game Development with C# — Alan Thorn
    Apress Berkeley, 2014
Reference Books
0
  • Interaction Design: beyond Human Computer Interaction — Preece, Rogers, Sharp
    John Wiley & Sons
  • Brian Fling — Mobile Design and Development
    O’Reilly Media, 2009
E-Resources
0
  • NPTEL — Mobile Design (preview)
    onlinecourses.nptel.ac.in
  • NPTEL — (CSL25) Course Preview
    onlinecourses.nptel.ac.in
Materials
0
  • Unit-1 Material
  • Unit-1 Assignment
  • Unit-2 Material
  • Unit-3 Material
  • Unit-4 Material
  • Unit-5 Material
  • Unit-3 Class Test-1
  • Open Book Exam-17-10-2025
Activity Based Learning (Suggested Activities in Class)
Activity 1. Demonstration of solution to a problem using different human interface model.
Activity 2. Real world problem solving with various human interface design models.
Activity 3. Implementation and presentation of research projects in HCI.

Research Methodology & Responsible AI Ethics in Computing

[As per Choice Based Credit System (CBCS) scheme]
Department of Computer Science and Engineering
Instructor: Dr. Gokulakrishnan S
Course Code: 24CS5502
Hours / Week: 03 Hours
L-T-P-J: 2–0–2–0
Credits: 03
Total Contact Hours: 39 Hours
Semester: V
CIA Marks WhatsApp Group Assignment_Notice
Course Learning Objectives (CLOs)
CLO1. Understand fundamental and advanced research methods in computing.
CLO2. Familiarize with intellectual property rights, copyright, and patents.
CLO3. Apply modern research tools for literature survey, writing, and plagiarism checking.
CLO4. Formulate research problems, hypotheses, and publishable outcomes with modern AI Ethics.
Unit-wise Topics
UNIT — I (06 Hours)
Introduction to Research
Meaning and objectives of research, Types of research: Fundamental, Applied, Experimental, and Descriptive, Criteria of good research, Ethics in research and academic integrity, Case studies in computing research.
UNIT — II (09 Hours)
Research Problem Formulation & Design
Problem identification and research gap analysis, Hypothesis: Types, formulation, testing, Research design: Exploratory, experimental, simulation-based studies, Data sources: Primary vs. Secondary.
UNIT — III (07 Hours)
Research Tools and Techniques
Literature review tools: Google Scholar, Scopus, Web of Science, ResearchGate; Citation managers: Zotero, Mendeley, EndNote; Plagiarism tools: Turnitin, Grammarly, PlagScan; Writing tools: LaTeX, Overleaf; Technical writing format (IEEE/ACM/Elsevier/SCI).
UNIT — IV (09 Hours)
Intellectual Property Rights (IPR)
Copyrights, patents (WIPO/IPINDIA), trademarks, open-access models, fair use; Legal and ethical issues of AI-generated content (e.g., ChatGPT).
UNIT — V (08 Hours)
Research Communication & Dissemination
Structure of papers, abstract writing, journal vs conference, indexing, h-index, peer review process; Ethical dissemination, responsible use of generative AI in publications.
Course Outcomes (COs)
CO1. Explain the fundamentals of research and ethics in computer science.
L2
CO2. Apply modern tools for literature review, citation, and plagiarism checking.
L3
CO3. Demonstrate understanding of copyright, patents, and research data management.
L2
CO4. Formulate research questions and hypotheses using suitable methodologies.
L4
CO5. Draft technical papers with structured content and clarity for publication.
L4 & L6
Evaluation Components:
Component Details Marks
MSE-1 Mid-Semester Exam 30
Paper Drafting / Publication Research Output 40
Assignment-1 Unit 1 & 2 10
Assignment-2 Unit 3 & 4 10
Open Book Exam [OBE] Unit 5 10
Total 100
Text Books
2
  • Research Methodology: Methods and Techniques
    Kothari, C.R. & Garg, G. (2019, 4th Ed.)
  • Research Methodology: A Step-by-Step Guide for Beginners
    Ranjit Kumar (2022, 5th Ed.)
References
6
  • Research in Education
    Best, J.W. & Kahn, J.V. (2014, 10th Ed.)
  • Research Design: Qualitative, Quantitative, and Mixed Methods
    Creswell, J.W. & Creswell, J.D. (2017, 5th Ed.)
  • WIPO – Understanding Industrial Property
    WIPO Publication No. 895E
  • OECD Principles on Artificial Intelligence
    OECD (2019)
  • Stanford HAI – AI Index Report
    Annual AI Index Report
  • IEEE Author Center
    Guidelines for Authors
Materials
6
  • Unit-1
  • 1. Meaning of Research
  • Unit-2
  • Unit-1 & 2 Mind Map
  • Unit-1 & 2 Surprise Test
  • Unit-1 & 2 Research Match-Up Worksheet-Student
  • Unit-1 & 2 Research Match-Up Worksheet-Faculty
  • Unit-1 & 2 Criteria of Good Research – Self-Check
  • Unit-3
  • Unit-3 Mind Map
  • Unit 1 and 2 Assignment-1
  • Unit 3 Assignment-2
  • RM_Important_Questions
  • Unit-4 Notes
  • Unit-5 Notes
  • Open Book Exam-Unit-5
Group Assignment – Drafting a Mini Research Paper
10
  • Assignment – Drafting a Mini Research Paper (40 Marks)
  • Master Guide
  • How to Draft the Paper
  • IEEE Paper Format
  • Springer MS Word Paper Format
  • Springer LaTeX Paper Format
  • Sample IEEE Paper
  • Sample Springer Paper
  • Sample Plagiarism Report
  • AI-Driven Wearable Plant Disease Recognition Systems- Review Paper
  • Team Details – Drafting a Mini Research Paper
AI-Based Tools for Research Methodology (Free and Open-Source):
  • Zotero – Smart reference manager with PDF annotation
  • Scite.ai – Citation support vs contrast insights
  • Connected Papers – AI-based citation graph visualizer
  • Research Rabbit – Citation & discovery engine
  • Consensus.app – Answers research queries from peer-reviewed papers
  • Elicit.org – Literature review assistant
  • Semantic Scholar – AI-powered academic search engine
  • Quillbot – Paraphrasing and grammar AI
  • Grammarly – Grammar and tone assistance
  • Turnitin Draft Coach – Plagiarism detection (if institution-supported)
Case Studies for Mini Projects and Assignments:
  • Patent Search & Ethics: Search patents for emerging tech (e.g., AI in education). Discuss ethical issues around misuse, surveillance.
  • AI Tool Comparison: Compare Semantic Scholar vs Elicit for a topic. Analyze transparency, bias.
  • Drafting a Paper: Write using Overleaf, Zotero, Grammarly, Turnitin.
  • Impact Analysis: Use Connected Papers to map citations of a top-cited paper.
  • Open Access vs Traditional Publishing: Compare arXiv vs IEEE on access and licensing.
Module-wise Reading and Reference:
  • Module 1: Stanford CS Research Primer; MIT OCW – Responsible Research; Kothari: Ch 1–2
  • Module 2: IIT Bombay NPTEL; Ranjit Kumar: Ch 3–4; Kothari: Ch 5–6
  • Module 3: Stanford Libraries; IIT KGP on LaTeX/Plagiarism; Kothari Ch 7–8; LaTeX Wikibook
  • Module 4: WIPO Academy; NPTEL IPR Lectures; Kothari Ch 9; WIPO Handbook
  • Module 5: Elsevier Researcher Academy; IEEE Author Center; Ranjit Kumar: Ch 12–13
Examinations
Mid Semester Test 1: October 10th to 13th

Fundamentals of Critical Thinking

[As per Choice Based Credit System (CBCS) scheme]
Department of Computer Science and Engineering
Instructor: Dr. Gokulakrishnan S
Course Code: 22LS0010
Hours / Week: 01 Hour
L-T-P-J: 1–0–0–0
Credits: 01
Total Hours: 13 Hours
Semester: III
Attendance WhatsApp Group
Course Learning Objectives (CLOs)
CLO1. Enable a framework to think critically about core subjects for better decisions with fewer mistakes.
CLO2. Motivate and inculcate transformational learning for a research mindset and effective teamwork.
Unit-wise Topics
UNIT — I (03 Hours)
Etymology and Definitions
Needs, enablement of knowledge and methods.
UNIT — II (02 Hours)
Common Denominators
Identifying shared structures across disciplines and frameworks.
UNIT — III (02 Hours)
Tests and Values
Evaluating ideas through critical tests, values, and reasoning.
UNIT — IV (02 Hours)
Research Findings in Problem Solving
Identifying key findings from research surveys and applying them in problem solving.
UNIT — V (02 Hours)
Problem-Solving Approaches
Strategies and methods to approach and resolve problems effectively.
UNIT — VI (02 Hours)
Case Study — Scenario Presentations
Jigsaw learning method applied to real-world scenarios and presentations.
Course Outcomes (COs)
CO1. Recognize definitions of knowledge and methods, articulate ideas and arguments.
L2
CO2. Develop a systematic critical thinking approach for assumption evaluation and hypothesis design.
L3
Reference Books
4
  • W. Baytiyeh & M.K. Naja (2017)
    Students’ Perceptions of Flipped Classroom – EJEE
  • Chang, P. & Wang, D. (2011)
    Cultivating Engineering Ethics and Critical Thinking – EJEE
  • Godfrey et al. (2014)
    Systems Thinking in Engineering Education – IJEE
  • Fink, A.
    Conducting Research Literature Reviews (5th ed.), UCLA
Materials
2
  • Introduction Slide
    Critical Thinking – Intro
  • Icebreaker Puzzles – Activity
    Activity-1
  • Critical_Thinking_75_Scenarios_Workbook
    Activity-2
  • Critical_Thinking_Assignment
    Activity-2
  • UNIT -IV-Materials
    Activity-2
  • Critical Thinking/Worksheet – Research Findings in Problem Solving
    Activity-2

C Programming for Problem Solving

[As per Choice Based Credit System (CBCS) scheme]
Department of Computer Science and Engineering
Instructor: Dr. Gokulakrishnan S
Course Code: 24EN1202
Hours / Week: 02 Hours
L-T-P-J: 1–0–2–0
Credits: 02
Total Contact Hours: 26 Hours
Semester: II
📅 B8-Lab Examination

Date: November 17th, 2025 (Monday)

📅 B17-Lab Examination

Date: November 13th, 2025 (Thursday)

📅 B18-Lab Examination

Date: November 14th, 2025 (Friday)

B8-CIA Marks B17-CIA Marks B18-CIA Marks Lab Exam Questions
Course Learning Objectives (CLOs)
CLO1. Understand the fundamentals of C programming language and problem-solving techniques.
CLO2. Apply control structures, functions, arrays, pointers, and string manipulation for solving computational problems.
CLO3. Develop modular programs using user-defined functions and recursion.
CLO4. Implement solutions to practical problems through structured programming concepts.
Text Book
1
  • Programming in C
    Textbook (linked in course assets)
Materials
3
  • Introduction PPT
    C Overview — First Hour
  • Unit-1 PPT
    Unit-1 slides (assets/CDSS Lab/Unit-1.pptx)
  • Laboratory Environment
    Setup guide & tips
List of Laboratory Experiments
Experiment 1.A
A. Write a C program to read two whole numbers, store them in variables, and display their values with variable names. Then, swap their contents and print the updated values.
Experiment 1.B
B. Write a C program to compute simple interest using user-input values for the principal amount, interest rate, and term. Display the calculated interest amount.
Experiment 1.C
C. Write a C program to calculate and display the factorial of a number entered by the user.
Experiment 2
Write a C program to generate the roots of a quadratic equation. The various parameters for the quadratic equation are to be received as inputs from the user.
Experiment 3.A
A. Write a C program to read a number and check whether the entered number is a palindrome.
Experiment 3.B
B. Write a C program to calculate the ‘n’th power of a given number ‘x’. Take both ‘n’ and ‘x’ as inputs from the user.
Experiment 4.A
A. Write a C program to generate the ‘Fibonacci’ series for ‘n’ positions, take ‘n’ as an input from the user.
Experiment 4.B
B. Write a C program that takes two numbers ‘x’ and ‘y’ as input and calculates the Greatest Common Divisor (GCD) of them and displays the result.
Experiment 5
Write a C program that takes two numbers, x and y, as input and stores them in separate variables. The program should then read a single character representing an operator (+, -, *, or /). It should perform the corresponding arithmetic operation (x op y), display the result, and then wait for another operator input. If the user enters 'q' instead of one of the four operators, the program should terminate.
Experiment 6.A
A. Write a C program that reads a string from the user and prints the length of the string. Program must implement the logic to calculate the length and not use a library function.
Experiment 6.B
B. Write a C program that reads two strings, joins them into another variable, and prints the concatenated result. Use static memory allocation with a fixed size.
Experiment 6.C
C. Write a C program that takes a string as input from the user, reverses it, and then prints the reversed string.
Experiment 7
Write a program that reads ‘n’ numbers and stores them in an array in the same order as entered. First, it prints the numbers in their original order. Then, it sorts the array in ascending order and prints the sorted numbers.
Experiment 8
Write a program that searches for a specific number in a pre-initialized array. The number to be searched is taken as input from the user, and the program prints an appropriate message indicating whether the number is found or not.
Experiment 9
Write a program that uses a function to swap two numbers. The function takes as input the pointers to the two variables that contain the values to be swapped.
Experiment 10
Write a program that can parse a simple arithmetic expression of type (x op y op z). The program can make use of all the available string manipulation functions to parse the string efficiently. The arithmetic expression is given as an input by the user. ‘x’, ‘y’, ‘z’ are all integers. ‘op’ can be one of ‘+’, ‘-‘, ‘*’, ‘/’.
Open-Ended Experiments
Experiment 1
Write a C program to show the Roman number representation of a given number.
Experiment 2
Write a C program to implement the Tower of Hanoi problem using recursion. The program should prompt the user to enter the number of disks and then display the sequence of moves required to transfer all disks from the source peg to the destination peg using an auxiliary peg, following the rules of the Tower of Hanoi.
Experiment 3
Write a menu-driven C program to perform the following operations on strings: • Read a string. • Display the string. • Merge two strings. • Copy n characters starting from the Mth position. • Calculate the length of the string. • Count the number of uppercase letters, lowercase letters, numbers, and special characters in the string. • Count the number of words, lines, and characters in the string. • Replace all occurrences of comma (’,’) with semicolon (‘;’) in the string. • Exit the program.
Experiment 4
Write a C program to implement a Library Management System that allows users to: • Add new books with details like Title, Author, ISBN, and Availability Status. • Search for a book by Title or Author. • Issue a book (update availability status). • Return a book. • Display the list of all books. • Exit the system.

M.Tech Data Science

Department of Computer Science and Engineering

Instructor: Dr. Gokulakrishnan S

[As per Choice Based Credit System (CBCS) scheme]

SEMESTER — III
Course Code23CSE5107Credits04
Hours / Week03 HoursTotal Hours39 (T) + 26 (P) Hours
L-T-P-J3–0–2–0
Course Learning Objectives:
  1. Apply processes suitable to data preprocessing and transformation to prepare data for insights.
  2. Visualize data using graphs and plots to identify relationships and patterns and to model EDA.
  3. Utilize mathematical and statistical techniques to test hypotheses.
  4. Employ central limit theorem and confidence intervals to model real-world phenomena and support predictions.
  5. Use open-source tools to apply data-driven problem solving and identify solutions.
Unit-wise Topics:
  1. UNIT — I: About Data (09 Hours)
    Introduction; Causality and Experiments; Data Pre-processing: Knowing data, data cleaning, reduction, transformation, discretization.
  2. UNIT — II: Data Visualization (09 Hours)
    Visualizing categorical and numerical distributions; Overlaid Graphs; EDA with plots and summary statistics; Histograms; Stem-and-Leaf; Quantile-based plots; Continuous distributions; Quantile plots; QQ plot; Box plots.
  3. UNIT — III: Statistics (06 Hours)
    Sampling; Sample means/variance/moments; Covariance, correlation; Sampling distributions; Parameter estimation, bias, MSE, relative efficiency, standard error, MLE; Empirical distributions; Testing hypotheses; Model assessment; Multiple categories; Decisions and uncertainty; Comparing two samples; A/B testing; ANOVA.
  4. UNIT — IV: Sampling Theory (07 Hours)
    Estimation; Percentiles; Bootstrap; Confidence intervals and usage; SD and normal curve; Central Limit Theorem; Point and interval estimation; Prediction; Correlation; Regression line; Least squares.
  5. UNIT — V: Case Studies using Computational Tools (08 Hours)
    Visualization and analytics with tools such as Altair, Tableau, RapidMiner, MATLAB. Access to open-source tools for hands-on case studies.
Course Outcomes (COs):
  1. Apply data preprocessing and transformation to prepare data for insight extraction. (L3)
  2. Create visualizations to identify relationships and patterns and perform EDA. (L3)
  3. Use statistical techniques for hypothesis testing, covariance, A/B testing, and ANOVA. (L3)
  4. Employ CLT and confidence intervals for modeling and predictions. (L2 & L3)
  5. Use open-source tools to formulate problems, identify solutions, and build models. (L4)
Evaluation Components:
Component Details Marks
MSE-1 Mid-Semester Exam 15
MSE-2 Mid-Semester Exam 15
Assignment-1 Units 1 & 2 10
Assignment-2 Units 3 & 4 10
Open Book Exam [OBE] Unit 5 10
Lab/Case Studies Hands-on analytics using open-source tools 20
SEE Semester End Examination 20
Total 100
TEXT BOOKS:
  1. Adi Adhikari & John DeNero, Computational and Inferential Thinking: The Foundations of Data Science, 1st ed., 2019.
  2. Jiawei Han, Micheline Kamber, Jian Pei, Data Mining: Concepts and Techniques, 4th ed., Elsevier, 2006.
  3. Douglas C. Montgomery, George C. Runger, Applied Statistics and Probability for Engineers, 6th ed., Wiley, 2013.
REFERENCE BOOKS:
  1. Wendy L. Martinez, Angel R. Martinez, Computational Statistics Handbook with MATLAB, 2nd ed., Chapman & Hall/CRC, 2008.
E-Resources:
  1. Data Science for Engineers (IIT Madras) – https://nptel.ac.in/courses/106106179
  2. IFACET Data Science Program (IIT Kanpur) – https://ifacet.iitk.ac.in/professional-certificate-course-in-data-science/
  3. Online Statistics Education – https://online.stat.psu.edu/stat506/lesson/1/1.4
  4. Advanced Graphs & QQ Plots – https://onlinestatbook.com/2/advanced_graphs/q-q_plots.html
Teaching-Learning Process (Suggested):
Activity Based Learning (Suggested Case Studies)
Examinations:
Materials:
Course Exit Survey:

Artificial Intelligence and Machine Learning

[As per Choice Based Credit System (CBCS) scheme]
Department of Computer Science and Engineering
Instructor: Dr. Gokulakrishnan S
Course Code: 24CS5601
Hours / Week: 03 Hours
L-T-P-J: 2–0–2–0
Credits: 04
Total Contact Hours: 45 Hours
Semester: V
Attendance WhatsApp Group Course Drive
Course Learning Objectives (CLOs)
CLO1. Understand the basic concepts and techniques in Artificial Intelligence.
CLO2. Explore different forms of learning and Artificial Neural Networks.
CLO3. Formulate machine learning problems corresponding to different applications.
CLO4. Evaluate the performance of various models generated from data.
CLO5. Apply algorithms to real problems, optimize models, and report accuracy.
Unit-wise Topics
UNIT — I (08 Hours)
INTRODUCTION & INTELLIGENT AGENTS
AI Foundations, History, Risks/Benefits, Agents & Environments, Rationality, Nature of Environments, Structure of Agents. Textbook 1: Ch 1–2
UNIT — II (08 Hours)
SEARCH, LOGIC & LEARNING
Problem-solving agents, Knowledge-based agents, Logic, Propositional Logic, Forms of Learning, Neural Networks (Perceptron, Multilayer, Deep Learning). Textbook 1 & 2: Ch 3,7,19; Ch 11
UNIT — III (08 Hours)
REGRESSION & CLASSIFICATION
Linear & Non-linear Regression, Logistic Regression, Naïve Bayes, KNN, SVM, Decision Tree. Textbook 2 & 3: Selected chapters
UNIT — IV (08 Hours)
CLUSTERING & ENSEMBLE METHODS
K-Means, Gaussian Mixtures, Hierarchical Clustering, Voting, Bagging, Boosting, Random Forest, Gradient Boosting. Textbook 2 & 3: Selected chapters
UNIT — V (07 Hours)
EXPERIMENT DESIGN & ANALYSIS
Experimental design principles, Cross-validation, Resampling, Classifier Performance, Hypothesis Testing, ANOVA. Textbook 2: Ch 19
Course Outcomes (COs)
CO1.Understand AI foundations and concepts of intelligent agents.
L2
CO2.Model perceptron to learn Boolean functions.
L3
CO3.Develop supervised learning solutions (regression & classification).
L3
CO4.Apply clustering and ensemble methods.
L3
CO5.Evaluate ML algorithms.
L3
CO6.Develop Python ML models and analyze performance.
L4
Laboratory Experiments
Lab 1
Data Collection and Preprocessing
Lab 2
Exploratory Data Analytics
Lab 3
Linear & Multivariate Regression
Lab 4
Logistic Regression
Lab 5
Decision Tree Classification
Lab 6
Naïve Bayes
Lab 7
K-Nearest Neighbors (KNN)
Lab 8
Support Vector Machine (SVM)
Lab 9
K-Means Clustering
Lab 10
Random Forest
Lab 11
XGBoost
Lab 12
Mini Project
Text Books
3
  • Russell & Norvig, Artificial Intelligence: A Modern Approach, Pearson, 4th Ed.
  • Ethem Alpaydin, Introduction to Machine Learning, MIT Press, 3rd Ed.
  • Hastie, Tibshirani, Friedman, The Elements of Statistical Learning, Springer, 2nd Ed.
Reference Books
3
  • Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer.
  • Marc Peter Deisenroth et al., Mathematics for Machine Learning, CUP.
  • Thomas M. Mitchell, Machine Learning, McGraw Hill.
E-Resources
3
  • Kaggle – Intro to ML
  • Microsoft Learn – ML
  • NPTEL – Machine Learning
Materials
5
  • Machine Learning Syllabus
  • 2. Exploratory Data Analytics Program
  • 2. Exploratory Data Analytics Data Set
  • 3. Linear Regression Program
  • 3. Linear Regression Python Code
  • 3. Linear Regression Program Data Set
  • df = df.drop(columns=[col for col in ['Unnamed: 15', 'Unnamed: 16'] if col in df.columns], axis=1)
  • 4. Logistic Regression Program
  • 4. Logistic Regression Program Data Set

Amazon Web Services (AWS)

Department of Computer Science and Engineering

Instructor: Dr. Gokulakrishnan S

[As per Choice Based Credit System (CBCS) scheme]

SEMESTER — VII
Course Code21CS4708Credits03
Hours / Week03 HoursTotal Hours39 Hours
L-T-P-J3–0–0–0
Course Learning Objectives:
  1. Understand fundamentals of Cloud Computing, Amazon EC2 and Amazon EBS on AWS.
  2. Devise the process of services for Storage and Virtual Private Cloud in AWS.
  3. Develop Database services on the AWS platform.
  4. Recognize the importance of Authentication and Authorization for Security in AWS.
  5. Apply DNS and Network Routing for an EC2-based web server.
Unit-wise Topics:
  1. UNIT — I (08 Hours)
    Introduction to Cloud & AWS, EC2 and EBS: Cloud & Virtualization, Optimization, AWS Cloud & Platform Architecture, Reliability & Compliance, Working with AWS; EC2 Instances, Storage Volumes, Auto Scaling, Systems Manager, AWS CLI example.
  2. UNIT — II (08 Hours)
    Storage & Virtual Private Cloud: S3 architecture, Durability/Availability, Object Lifecycle, Accessing S3 objects, S3 Glacier, other storage; VPC concepts—CIDR, Subnets, ENIs, Internet Gateways, Route Tables, Security Groups, NACLs, Public/Elastic IPs, NAT, VPC Peering.
  3. UNIT — III (06 Hours)
    Database Services: RDS, Redshift; NoSQL with DynamoDB and use cases.
  4. UNIT — IV (09 Hours)
    Security, Monitoring & Audit: IAM and authentication tools; CloudTrail—Management/Data events, Trails, Log File Integrity Validation; CloudWatch—Metrics, Graphing, Metric Math, Logs, Alarms.
  5. UNIT — V (07 Hours)
    DNS, Routing & Messaging: Route 53, CloudFront (CDN), CLI example; SQS—queues, types, polling, dead-letter queues; Kinesis—Video Streams, Data Streams, Data Firehose (vs Data Streams).
Course Outcomes (COs):
  1. Utilize fundamentals of cloud computing, EC2, load balancing and auto scaling on AWS. (L3)
  2. Examine AWS Storage and VPC services and configurations. (L3)
  3. Design and develop database solutions using RDS/Redshift/DynamoDB. (L3)
  4. Implement IAM, CloudTrail and CloudWatch for secure, observable systems. (L3)
  5. Apply DNS, network routing, SQS and Kinesis in cloud applications. (L4)
Evaluation Components:
Component Details Marks
MSE-1Mid-Semester Exam15
MSE-2Mid-Semester Exam15
Assignment-1Units 1 & 210
AWS-CertificationAWS-Certification10
Open Book Exam [OBE]Unit 510
SEESemester End Examination40
Total 100
TEXT BOOKS:
  1. Ben Piper, David Clinton, AWS Certified Solutions Architect Study Guide: Associate SAA-C02 Exam, 2021.
REFERENCE BOOKS:
  1. Mark Wilkins, Learning Amazon Web Services (AWS), Pearson, 2019.
  2. Anthony J. Sequeira, AWS Certified Cloud Practitioner, Pearson, 2020.
E-Resources:
  1. EC2 User Guide – Concepts
  2. Amazon S3 – User Guide
  3. VPC API Reference
  4. Amazon CloudFront
Activity Based Learning (Suggested)
Examinations:
Materials:
Course Exit Survey:

Cloud Infrastructure Management

Department of Computer Science and Engineering

Instructor: Dr. Gokulakrishnan S

[As per Choice Based Credit System (CBCS) scheme]

SEMESTER — VIII
Course Code20CS4806Credits03
Hours / Week03 HoursTotal Hours39 Hours
L-T-P-J3–0–0–0
Course Learning Objectives:
  1. Understand cloud computing architecture, enterprise use cases, challenges, workflows, and architectural styles.
  2. Comprehend cloud enabling technologies including virtualization (full vs. para) and resource management/scheduling.
  3. Analyze cloud storage mechanisms and evaluate infrastructure components in cloud environments.
  4. Identify common security challenges and discuss security/privacy concerns for users, VMs, and shared images.
  5. Evaluate key technologies used in Xen VMM and various cloud applications.
Unit-wise Topics:
  1. MODULE — I: Cloud Infrastructure and Application Paradigms (09 Hours)
    Cloud at Amazon/Google/Microsoft; Open-source private clouds; Storage diversity & vendor lock-in; Energy/ecological impact; SLAs; UX & licensing; Challenges of cloud; Architectural styles; Workflows (state-machine coordination), ZooKeeper; MapReduce model.
    Textbook-1: Ch. 3 (67–95), Ch. 4 (99–115).
  2. MODULE — II: Virtualization and Resource Management & Scheduling (09 Hours)
    Layering & virtualization; VMMs and VMs; performance & security isolation; full vs. para-virtualization; hardware support; Policies & mechanisms for resource management; two-level allocation; fair queuing & variants; deadlines; MapReduce scheduling; dynamic scaling.
    Textbook-1: Ch. 5 (132–142), Ch. 6 (164, 182–201).
  3. MODULE — III: Cloud Storage Structure (07 Hours)
    Evolution of storage; models; file systems & databases; distributed file systems (GPFS, GFS, HDFS); Chubby locks; transaction processing & NoSQL; Bigtable, Megastore.
    Textbook-1: Ch. 8 (242–278).
  4. MODULE — IV: Cloud Security and Mechanisms (07 Hours)
    Cloud security risks; privacy & PIA; trust; OS & VM security; risks from shared images and management OS; trusted VMM.
    Textbook-1: Ch. 9 (274–298).
  5. MODULE — V: Case Studies (07 Hours)
    Grep-the-Web; science & engineering on cloud; HPC in cloud; social computing & digital content; Xen para-virtualization, optimizing network virtualization, vBlades; VM performance comparisons; pitfalls (“dark side”) of virtualization.
    Textbook-1: Ch. 4 (118–128); Ch. 5 (144–156).
Course Outcomes (COs):
  1. Examine cloud infrastructures at Amazon, Google, and Microsoft and analyze cloud challenges. (L4)
  2. Identify virtualization layers and apply suitable scheduling algorithms for resource management. (L3)
  3. Compare cloud file systems and analyze transactions using NoSQL databases. (L4, L5)
  4. Analyze security, privacy, and interoperability issues in cloud computing. (L4)
  5. Evaluate alternative cloud solutions for varied applications. (L5)
Evaluation Components:
Component Details Marks
MSE-1Mid-Semester Exam15
MSE-2Mid-Semester Exam15
Assignment-1Modules 1 & 210
AWS CertificationAWS Certification10
Open Book Exam [OBE]Module 510
SEESemester End Examination40
Total 100
TEXT BOOKS:
  1. Dan C. Marinescu, Cloud Computing: Theory and Practice, Elsevier (MK), 2013.
REFERENCE BOOKS:
  1. Rajkumar Buyya, James Broberg, Andrzej Goscinski, Cloud Computing: Principles and Paradigms, Wiley, 2014.
  2. John W. Rittinghouse, James F. Ransome, Cloud Computing: Implementation, Management, and Security, CRC Press, 2013.
E-Resources:
  1. Apache Hadoop Documentation
  2. The Google File System (Ghemawat et al.)
  3. Apache ZooKeeper Documentation
Activity Based Learning (Suggested)
Examinations:
Materials:
Course Exit Survey:

Spring Boot

Department of Computer Science and Engineering

Instructor: Dr. Gokulakrishnan S

[As per Choice Based Credit System (CBCS) scheme]

SEMESTER — V
Course Code—Credits02
Hours / Week02 HoursTotal Hours13 (L) + 26 (P) Hours
L-T-P-J1–0–2–0
Course Learning Objectives:
  1. Understand Spring Boot concepts, RESTful APIs, JPA-based persistence, security, testing, deployment, and microservices with Spring Cloud.
  2. Develop and manage scalable Java web applications using Spring Boot covering core concepts, data access, security, testing, and deployment.
  3. Explain the role of Spring Boot and frameworks, recognize API development importance, and identify features for real-world scenarios.
Unit-wise Topics:
  1. UNIT — I: Introduction to Spring Boot (04 Hours)
    Spring & Spring Boot overview, history & benefits; traditional Spring vs Spring Boot; environment setup (Java, Maven/Gradle, IDE); creating a basic app; starters & dependencies; project structure; @SpringBootApplication and configuration files.
    Textbook 1: Ch 1 (1.1–1.4)
  2. UNIT — II: Application Development & REST (06 Hours)
    DI & IoC; configuration properties & profiles; externalized configs; auto-configuration (understanding/customizing); REST principles; building controllers with @RestController, @RequestMapping, @GetMapping, @PostMapping; consuming REST with RestTemplate and Traverson.
    Textbook 1: Ch 2.1; Ch 5 (5.1–5.3); Ch 6 (6.1–6.3); Ch 7 (7.1–7.2)
  3. UNIT — III: Data Access & Security (06 Hours)
    Spring Data JPA: config, repositories, CRUD, JPQL/native queries, pagination/sorting, relationships (1–1, 1–M, M–M). Security: enabling Spring Security; in-memory/JDBC/LDAP user stores; custom auth; securing requests; custom login; logout; CSRF; user details; transaction management.
    Textbook 1: Ch 3 (3.1–3.2); Ch 4 (4.1–4.4)
  4. UNIT — IV: Testing, Actuator & Reactive APIs (06 Hours)
    Testing with Spring Boot; Actuator; microservices basics; Spring Cloud overview; WebFlux reactive programming—controllers, functional handlers; testing reactive controllers (GET/POST, live server), consuming APIs reactively, error handling; securing reactive APIs.
    Textbook 1: Ch 11 (11.1–11.5)
  5. UNIT — V: Reactive Data Persistence (04 Hours)
    Spring Data reactive programming; converting reactive/non-reactive types; reactive repositories; Cassandra: enabling Spring Data Cassandra, data modeling, mapping, reactive repositories; MongoDB: enabling Spring Data MongoDB, mapping, reactive repositories.
    Textbook 1: Ch 12 (12.1–12.3)
Course Outcomes (COs):
  1. Understand Spring Boot activities and analyze the framework. (L1 & L2)
  2. Apply Spring Boot to develop RESTful web services. (L3)
  3. Describe Spring Data JPA and implement transaction management for secure, consistent apps. (L2)
  4. Develop, test, and secure reactive APIs using Spring WebFlux. (L2 & L3)
  5. Build and manage reactive data persistence using Spring Data for Cassandra and MongoDB. (L3)
Evaluation Components:
Component Details Marks
AssignmentCIA10
Class TestCIA10
CertificationCIA10
Lab/ProjectHands-on Spring Boot implementation60
Open Book Exam [OBE]Unit 510
Total 100
TEXT BOOKS:
  1. Craig Walls, Spring in Action, 5th Edition, Manning, ISBN 9781617294945.
REFERENCE BOOKS:
  1. Santosh Kumar K., Spring and Hibernate, Tata McGraw-Hill, 2009, ISBN 9780070680111.
  2. Paul Tepper Fisher, Brian D. Murphy, Spring Persistence with Hibernate, Apress, 2010, ISBN 9781430226321.
  3. Amritendu De, Spring 4 and Hibernate 4: Agile Java Design and Development, McGraw-Hill, 2015, ISBN 9780071845113.
  4. Chris Schaefer, Clarence Ho, Rob Harrop, Pro Spring, Apress.
E-Resources:
  1. Spring 5 with Spring Boot 2 (Udemy)
  2. Spring Boot Playlist (YouTube)
Teaching-Learning Process (Suggested):
Activity Based Learning (Suggested)
Materials:
Course Exit Survey: