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UG Courses (catalog)
PG Courses (catalog)
PG Courses (AI)
PG Courses (CSE)
UG Courses (catalog)
Responsive Course Table
Core Course:
Code Course Name No. of Credit Brief List of Topics Detailed Course Template
CS110 DISCRETE MATHEMATICAL STRUCTURES 3 Topics include sets, relations, functions, logic and proofs, mathematical induction, combinatorics and basic graph theory. –
CS201 DATA STRUCTURES 4 This course introduces the basics of data structures and algorithms. Topics span across basic data structures (e.g., arrays, stacks, linked lists, queues, heaps, etc.), algorithm analysis (e.g., asymptotic analysis, master theorem, etc.), search Trees (e.g., AVL Trees, redblack trees, etc.), sorting (e.g., quick sort, linear time sorting, order statistics, etc.), hashing (e.g., dictionaries, tries, etc.) and Graphs (e.g., graph data structures, graph traversal algorithms such as BFS and DFS, minimum spanning Tree, shortest path algorithms, etc.) Click here to view
CS202 PROGRAMMING PARADIGMS AND PRAGMATICS 4 Introduction: Motivation to study concepts of programming languages and introduction to major language families Compiler Design: Introduction to Regular Expressions, Syntax Analysis, Lexical Analysis. Implementation using Lex/Yacc. Foundations: Covers fundamental semantic issues of variables with emphasis on Names, Binding,Scope, Lifetime and Type Checking , Core Design Constructs: Covers issues that lie at the core of most programming languages: Data Types, Control Flow, Subroutines, Abstraction & Object Orientation, Alternative Programming Models:Introduction to Functional and Logic Programming Languages, Concurrency: Basic introduction to the fundamentals of Concurrency including communication, synchronization, creation and implementation of threads. Click here to view
CS203 DIGITAL LOGIC DESIGN 4 Digital System: Introduction to digital logic and digital system, digital logic states, number systems,Boolean algebra and logic minimization: Boolean functions, expressions, minimizations of Boolean functions, K maps, QM method, two level and multiple level logic. Combinational logic design: Basic components: adder, multiplexer, decoder, ROMs, programmable logic, Introduction to HDL and FPGA. Arithmetic circuits. Synchronous sequential logic design: Flip-flops and latches, finite state machines – mealy and moore, state assignment and state minimizations. Counters, registers, and memories. Top down approach of digital design: Data path-control path design, register transfer level design, pipelining and parallelism. Case studies. Advanced issues in digital design: Parallelism, IP blocks and reuse,timing issues, faults and testing. Technology aspects: FPGA and ASICs, logic mapping and binding, introduction to logic synthesis and EDA tools Click here to view
CS302 ANALYSIS AND DESIGN OF ALGORITHMS 3 This course goes deep into the design and analysis of algorithms. Topics include: advanced data structures (e.g., interval and range trees, segment trees, splay trees), divide and conquer techniques (e.g., karatsuba integer multiplication algorithm, mergesort, strassen’s matrix multiplication algorithm, fast fourier transform, etc.), dynamic programming (e.g., longest increasing subsequence, knapsack problem, matrix chain multiplication, bellman-Ford and Floyd-Warshall algorithms, etc.), greedy algorithms (e.g., minimum spanning trees, huffman coding, etc.), network flows (e.g., ford-fulkerson algorithm, bipartite matching, etc.), NP-completeness reductions and miscellaneous algorithms (e.g., number-theoretic algorithms, euclid’s algorithm, modular arithmetic, etc.) Click here to view
CS306 THEORY OF COMPUTATION 3 This course provides an introduction to the foundations of theoretical computer science. Topics include automata theory (finite automata, regular expressions, context-free grammars, and pushdown automata), computability theory (Turing machines, decidability, and the halting problem), and complexity theory (time and space complexity, P vs NP, and NP-completeness). Click here to view
CS307 COMPUTER ORGANIZATION AND ARCHITECTURE 4 Introduction: Computer Organization and computer architecture, review of fundamentals of digital logic design. Arithmetic Logic Unit: Arithmetic operations on binary numbers, ALU design, floating point arithmetic. Processor Design: Introduction to instruction set architecture, addressing modes and formats, instruction set encoding, instruction execution cycle. Pipeline design: Pipeline processor, hazard detection, hazard avoidance and hazard resolution techniques, data hazards and pipeline interlock, data forwarding, branch hazards, branch prediction, control hazards. Memory design: Memory organization, Virtual memory, Cache memory, memory level parallelism, cache coherence, shared memory multiprocessors, multithreaded processors. Input/output devices and interaction: Input output devices, interrupt handling, storage and I/O devices, performance considerations. Advanced architectural techniques: Instruction level parallelism, static and dynamic branch prediction, code scheduling, out of order execution, speculative execution, hardware multithreading, VLIW processor, software pipelining, memory system prospective: Memory hierarchy, cache memory, memory level parallelism, multiple memory banks, advanced cache memory designs. Parallel and multi core processors: Flynn’s taxonomy, review of contemporary processors and multicore architectures. Review of advances in computer architecture and special topics: RISC/CISC debate, power awareness, real world case studies. Introduction to research papers in computer architecture and case studies. Current research areas and course summary. Laboratory Experiments: 1. Design and implementation of a functional unit of a single cycle processor. 2. Design and implementation of a functional unit of a pipelined processor. 3. Design and implementation of a functional unit of a multicore processor. 4. Case study on a given processor architecture and their code optimizations. System prospective: Input/output devices, DMA, busses and interconnect system on chip, multi processors. Click here to view
CS301 DATABASES 4 This course covers some of the fundamental topics relating to Database Systems including relational model, SQL queries, ER models, functional dependency theory, normalization, file structures, index structures, query processing algorithms, query optimization, transaction processing and some trend topics (e.g., Data Warehousing, Deductive databases, etc.). Click here to view
CS303 OPERATING SYSTEMS 4 This course covers some of the fundamental topics relating to Operating Systems. Topics include processes (process concept, multithreading and Scheduling), synchronization techniques, memory management techniques, file systems, Input/output implementation and management. Click here to view
CS304 COMPUTER NETWORKS 4 This course covers some of the fundamental topics relating to computer networks. Topics include: introduction to protocol layering, TCP/IP architecture, circuit switching, packet switching, access networks, physical media, roles of different layers, data link layer (address resolution protocol, Wireless LAN’s, error detection and correction, etc.), network layer (routing versus forwarding, routing in the Internet, Internet Protocol, multicast routing, etc.), transport layer (TCP/IP, UDP, principles of reliable data transfer, connection-oriented transport TCP, flow control and congestion control), application layer (world wide web, file transfer protocol, socket programming, etc.), network security, wireless and mobile networks. Click here to view
CS305 SOFTWARE ENGINEERING 4 Topics include software processes (software development lifecycle), software process models and activities, modeling (requirements, design concepts and modeling), design and implementation (Object-oriented design using UML), implementation issues and use of design patterns, software testing (regression testing, Integration testing, test generation), software management (estimation techniques, Configuration management) Click here to view
PG Courses (catalog)
Core Course:
Code Course Name No. of Credit Curently Offerings(Yes/No) Brief Description Course Template
CS526 Mathematical Foundations of Computer Science 3 – – Click here to View
CS527 Computer Systems 3 – – Click here to View
CS509 PG Software Lab 3 – Click here to View
CS506 Data Structure and Algorithms 3 – – Click here to View
CS500 PG Seminar in Computer Science – – – Click here to View
Elective Course:
Code Course Name No. of Credit Curently Offerings(Yes/No) Brief Description Course Template
CS500    PG SEMINAR IN COMPUTER SCIENCE _ _ Click here to View
CS501 COMPUTATIONAL COMPLEXITY 3 _ _ Click here to View
CS502 COMPUTATIONAL GEOMETRY 3 _ _ Click here to View
CS503 MACHINE LEARNING 4 _ Click here to View
CS504 ARTIFICIAL NEURAL NETWORK 3 _ _ Click here to View
CS505 DIGITAL IMAGING SYSTEMS 4 _ _ Click here to View
CS506 DATA STRUCTURES AND ALGORITHMS 3 _ Click here to View
CS507 MULTIMEDIA SYSTEMS 4 _
CS508 FOUNDATIONS OF COMPUTER SCIENCE 3 _ Click here to View
CS509 PG SOFTWARE LAB 3 – _ Click here to View
CS510 ADVANCED COMPUTER ARCHITECTURE 4 _ Click here to View
CS511 REAL TIME SYSTEMS 4 _ _ Click here to View
CS512 ARTIFICIAL INTELLIGENCE 4 _ _ Click here to View
CS513 ALGORITHMS IN BIOINFORMATICS 4 _ _ Click here to View
CS514 COMPUTER SYSTEM SECURITY 4 _ _ Click here to View
CS515 COMPUTER GRAPHICS _ _ Click here to View
CS516 WIRELESS AD-HOC NETWORKS 4 _ Click here to View
CS517 DIGITAL IMAGE PROCESSING & ANALYSIS 4 _ _ Click here to View
CS518 COMPUTER VISION 4 _ Click here to View
CS519 SYSTEM LEVEL DESIGN AND MODELLING 4 _ _ Click here to View
CS520 DATABASE SYSTEM IMPLEMENTATION 4 _ Click here to View
CS521 FUNDAMENTALS OF DATA SCIENCES 4 _ _ Click here to View
CS522 SOCIAL NETWORKS 4 _ _ Click here to View
CS523 APPLIED CRYPTOGRAPHY 4 _ Click here to View
CS 524 DATA MINING 4 _ _ Click here to View
CS 525 post-quantum crypto 3 _ _ Click here to View
CS 526 Mathematical Foundations of Computer Science 3 _ _ Click here to View
CS 527 Computer Systems 4 _ _ Click here to View
CS 528 Big Data Tools _ _ Click here to View
CS 529 Applied Artificial Intelligence 4 _ Click here to View
CS 530 Multi Agent Systems 3 _ _ Click here to View
CS 532 Security Analytics 3 _ _ Click here to View
CS 533 Reinforcement Learning 3 _ _ Click here to View
CS 534 Low Power Design 3 _ Click here to View
CS 535 Intro. to Game Theory and Mechanism Design 4 _ Click here to View
CS 536 GRAPH THEORY 3 – _ Click here to View
CS537 Synthesis of Digital Systems 4 – _ Click here to View
CS600 INDEPENDENT STUDY 4 _ _ Click here to View
CS601 APPROXIMATIONAL ALGORITHMS 4 _ _ Click here to View
CS602 RANDOMIZED ALGORITHMS 4 _ _ Click here to View
CS603 COMBINATORIAL OPTIMIZATIONS 4 _ _ Click here to View
CS604 ADVANCED OPERATING SYSTEMS 4 _ _ Click here to View
CS605 CONSTRAINT PROGRAMMING 4 _ _ Click here to View
CS606 ADVANCED SOFTWARE ARCHITECTURE 4 _ _ Click here to View
CS607 ADVANCED TOPICS IN CONTEMPRORY COMPUTING PLATFORMS 4 _ _ Click here to View
CS608 ADVANCED TOPICS IN INTERNET TECHNOLOGIES 4 _ _ Click here to View
CS609 NETWORK SCIENCE 4 _ _ Click here to View
CS610 ALGORITHMS EXEMPLIFIED 4 _ _ Click here to View
CS612 ADVANCED MACHINE LEARNING 4 _ _ Click here to View
CS613 GAME THEORY IN WIRELESS NETWORKS 3 _ _ Click here to View
CS615 BIOMEDICAL IMAGE PROCESSING & ANALYSIS 4 _ _ Click here to View
CS616 ADVANCED COMPUTER VISION 4 _ _ Click here to View
CS617 AFFECTIVE COMPUTING & INTERACTION 4 _ _ Click here to View
CS618 ARTIFICIAL NEURAL NETWORKS 4 _ _ Click here to View
CS619 ADVANCED ALGORITHMS 3 _ _ Click here to View
CS620 INTRODUCTION TO SPATIAL COMPUTING 4 _ _ Click here to View
CS621 PROBABILISTIC GRAPHICAL MODELS 4 _ _ Click here to View
CS622 ADVANCED IMAGE PROCESSING 4 _ _ Click here to View
CS698 M.TECH. PROJECT –I 12 _ _ Click here to View
CS699 M.TECH. PROJECT-II _ _ Click here to View
CS 724 ADVANCED DATA MINING 4 _ _ Click here to View
PG Courses (AI)
Programme Structure Programme structure Overall Structure of the Program
  • Specialization Core (20/22 credits):
    • CSE Core 14 credits  + at least 2 courses from Appendix A (i.e list of AI program core courses.
  • Elective Course credits (11 or more credits)
    • Specialization electives: At least 3 other courses from Appendix B (i.e.  list of AI program electives.
    • Department/Open Elective: One additional course i.e. 3/4 additional credits (any PG elective course). Even non-CSE PG course can be considered but with due approval from CSE RPEC and CSE HoD.
  • MTech Project (30 credits) :
    • One can register for the MTech project component only after successfully completing at least 21 course credits
    • MTech Project: Student interested in obtaining this specialization must undertake a MTech project in the area of Artificial Intelligence (or its allied areas, refer item 14)
Students will  be encouraged to take up interdisciplinary projects for fulfilling their MTP requirements. Relevance of this project with the theme of specialization would be decided by the steering committee of this specialization.
  • For the summer short semester, registration in non credited summer project or doing internship in an Industry is a mandatory component for the degree completion.
Max number of credits one can register in a semester = 24  Semester I
Sno Course No Course Title L-T-P-S-C Credits
1 CS506 Data Structures and Algorithms 3-1-2-6-4 4
2 CS526 Mathematics for Computer Science 3-1-0-5-3 3
3 CS527 Computer Systems 3-0-2-7-4 4
4 CS509 PG Software Lab 0-0-6-6-3 3 or 4
5 XXXXX Elective I —— 3 or 4
Semester II
S.no Course No Course Title L-T-P-S-C Credits
1 CSXXX AI Program core I —— 3 or 4
2 CSXXX AI Program core II —— 3 or 4
3 CSXXX Elective II —— 3 or 4
4 CSXXX Elective III —— 3
5 XXXXX Elective IV —— 3 or 4
6 CS500 PG Seminar in Compute Science —– 0 (S/U)
Semester III
S.no Course No Course Title L-T-P-S-C Credits
1 CS551 Colloquium Series —–  0 (S/U)
2 CS699 PROJECT-I 0-0-28-14-14 14
Semester IV
S.no Course No Course Title L-T-P-S-C Credits
1 CS555 PG Seminar-2 (Topics specific to one’s research project) —– 0 (S/U)
2 CS799 Project-2 0-0-32-16-16 16
  Area of Research for Internship/ Project :   Core topics in Artificial Intelligence (AI), Machine Learning (ML) and Data Mining (DM). Applications of AI/ML/DM in areas such as Internet of Things, Computer Vision and Image processing are also included. Appendix A- List of AI Program Core
S.no Course No Course Title L-T-P-S-C Credits
1 CS503 Machine Learning 3-0-2-7-4 4
2 CS512 Artificial Intelligence 3-0-2-7-4 4
3 CS521 Fundamentals of Data Sciences 2-0-2-5-3 3
4 CS524 Data Mining 3-0-0-6-3 3
5 CS504 Artificial Neural Networks (Deep Learning) 3-0-0-6-3 3
6 CS533 Reinforcement learning 3-0-0-6-3 3
Appendix B- List of AI Program Electives (at least 3 other courses from this list)
S.no Course No Course Title L-T-P-S-C Credits
1 CS503 Machine Learning 3-0-2-7-4 4
2 CS504 Artificial Neural Networks 3-0-0-6-3 3
3 CS507 Multimedia System 2-0-2-5-3 3
4 CS512 Artificial Intelligence 3-0-2-7-4 4
5 CS512 Artificial Intelligence 3-0-2-7-4 4
6 CS517 Digital Image Processing and Analysis 2-1-2-4-3 3
7 CS518 Computer Vision 2-0-2-5-3 3
8 CS521 Fundamentals of Data Science 2-0-2-5-3 3
9 CS522 Social Computing and Networks 2-0-2-5-3 3
10 CS524 Data Mining 3-0-0-6-3 3
11 CS530 Autonomous MultiAgent Systems 2-0-2-5-3 3
12 CS532 Security Analytics 2-0-2-5-3 3
13 CS533 Reinforcement Learning 2-0-2-5-3 3
14 CS535 Introduction To Game Theory And Mechanism Design 3-1-0-5-3 3
15 CS539 Internet of Things 3-0-0-6-3 3
16 CS545 Computer Graphics Lab 0-0-2-1-1 1
17 CS612 Advanced Machine Learning 2-0-2-5-3 3
18 CS615 Biomedical Image Processing & Analysis 2-0-2-5-3 3
19 CS616 Advanced Computer Vision 2-0-2-5-3 3
20 CS617 Affective Computing and Interaction 2-0-2-5-3 3
21 CS621 Probabilistic Graphical Models 3-0-0-6-3 3
22 CS623 Multimedia Surveillance Systems 2-0-2-5-3 3
23 CS720 Advanced Spatial Computing 3-0-0-6-3 3
24 CS724 Advanced Data Mining 2-0-2-5-3 3
25 CS546 INTRODUCTION TO CYBER PHYSICAL SYSTEMS 2-0-2-5-3 3
PG Courses (CSE)
Programme Structure
Programme structure  ( Overall Structure of the Program) Summary details of the Program Structure:
  • Core Courses credits : 14 credits
  • Elective course credits: 16 credits or more as per following conditions:
    • Any PG course offered in the CSE department.
    • Atmost one non-CSE PG course (with due approval from CSE RPEC and CSE HoD) can be considered.
  • 30 credits for the Project work course.
    • Project should be taken in CSE domain. One can register for the MTech project component only after successfully completing at least 21 course credits
    • For the summer short semester, registration in non credited summer project or doing internship in an Industry is mandatory component for the degree completion
Semester I
Sno Course No Course Title L-T-P-S-C Credits
1 CS506 Data Structures and Algorithms 3-1-2-6-4 4
2 CS526 Mathematics for Computer Science 3-1-0-5-3 3
3 CS527 Computer Systems 3-0-2-7-4 4
4 CS509 PG Software Lab 0-0-6-6-3 3
Semester II
S.no Course No Course Title L-T-P-S-C Credits
1 CSXXX Elective I —— 3
2 CSXXX Elective II —— 3
3 CSXXX Elective III —— 3 or 4
4 CSXXX Elective IV —— 3 or 4
5 XXXX Elective V —— 3 or 4
6 CS500 PG Seminar-1 —— 0 (S/U)
  Semester III
S.no Course No Course Title L-T-P-S-C Credits
1 CS551 Colloquium Series —— 0 (S/U)
2 CS699 Project-1 0-0-28-14-14 14
Semester IV
S.no Course No Course Title L-T-P-S-C Credits
1 CS555 PG Seminar-2 —— 0 (S/U)
2 CS799 Project-2 (Topics specific to one’s research project) 0-0-32-16-16 16
 
Area of Research for Internship/ Project : Project should done in one of the core topics in computer science and engineering domain. These topics largely span across theory, systems and applications branches of CSE.
Appendix A: List of CSE Electives**
S.no Course No Course Title L-T-P-S-C Credits
1 CS503 Machine Learning 3-0-2-7-4 4
2 CS504 Artificial Neural Networks 3-0-0-6-3 3
3 CS505 Digital Imaging Systems 2-0-2-5-3 3
4 CS507 Multimedia System 2-0-2-5-3 3
5 CS510 Advanced Computer Architecture 3-1-0-5-3 3
6 CS511 Real Time Systems 2-0-2-5-3 3
7 CS512 Artificial Intelligence 3-0-2-7-4 4
8 CS515 Computer Graphics 3-0-0-6-3 3
9 CS516 Wireless Ad-Hoc Networks 2-0-2-5-3 3
10 CS517 Digital Image Processing and Analysis 2-1-2-4-3 3
11 CS518 Computer Vision 2-0-2-5-3 3
12 CS519 System Level Design And Modelling 3-0-0-6-3 3
13 CS520 Database System Implementatio 3-0-2-7-4 4
14 CS521 Fundamentals of Data Science 2-0-2-5-3 3
15 CS522 Social Computing and Networks 2-0-2-5-3 3
16 CS523 Applied Cryptography 3-0-2-7-4 4
17 CS524 Data Mining 3-0-0-6-3 3
18 CS525 Post-Quantum Crypto 3-0-0-6-3 3
19 CS530 Autonomous MultiAgent Systems 2-0-2-5-3 3
20 CS533 Reinforcement Learning 2-0-2-5-3 3
21 CS535 Introduction To Game Theory And Mechanism Design 3-1-0-5-3 3
22 CS539 Internet of Things 3-0-0-6-3 3
23 CS540 Cryptocurrencies and Blockchain Technology 3-0-0-6-3 3
24 CS545 Computer Graphics Lab 0-0-2-1-1 1
25 CS601 Approximation Algorithms 3-0-0-6-3 3
26 CS602 Randomized Algorithms 3-0-0-6-3 3
27 CS603 Combinatorial Optimizations 3-0-0-6-3 3
28 CS604 Advanced Operating Systems 3-0-0-6-3 3
29 CS606 Advanced Software Architecture 2-0-2-5-3 3
30 CS607 Contemporary Computing Platforms 2-0-2-5-3 3
31 CS608 Topics in Internet Technologies 2-0-2-5-3 3
32 CS612 Advanced Machine Learning 2-0-2-5-3 3
33 CS615 Biomedical Image Processing & Analysis 2-0-2-5-3 3
34 CS616 Advanced Computer Vision 2-0-2-5-3 3
35 CS617 Affective Computing and Interaction 2-0-2-5-3 3
36 CS620 Introduction to Spatial Computing 3-0-2-7-4 4
37 CS621 Probabilistic Graphical Models 3-0-0-6-3 3
38 CS622 Advanced Image Processing 2-0-2-5-3 3
39 CS623 Multimedia Surveillance Systems 2-0-2-5-3 3
40 CS701 Special Topics in Complex Networks 3-0-2-7-4 4
41 CS702 Special Topics in Social Computing 3-0-2-7-4 4
42 CS720 Advanced Spatial Computing 3-0-0-6-3 3
43 CS724 Advanced Data Mining 2-0-2-5-3 3

S. Ramanujan Block,
Department of Computer Science and Engineering,
IIT Ropar,Bara Phool, Punjab 140001

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