M.Tech Curriculum
M. Tech (CSE) Curriculum - Dept. of Computer Science and Engineering - July 2020 onwards
Notes:
- The L-T-ET-P-O credits may vary for each CSE elective course; the total number of credits will
be 12.
- Project Phase I is a prerequisite for Project Phase II. Project Phase I will be evaluated by a PG committee in the month of November/December (the third semester in the program). The following are the three cases based on the Project Phase I grade:
- Grade C or above: Project Phase II (48 credits) in the 4th semester.
- Grade D and E: Project Phase IIA (12 credits) and 3 department electives (36 credits) in the 4th semester.
- Grade U: Repeat Project Phase I (48 credits) in the 4th semester. Then, do Project Phase IIA (12 credits) and 3 department electives (36 credits) in the 5th semester.
- For students with CGPA of 6.0 or below at the end of the second semester, the Faculty Advisor and the Head of the Department may recommend, on a case-by-case basis, that the student register for MTech Project Credits of 60 (CS5998 and CS6119) in the fourth semester or later, after completing 36 credits of Department electives.
- Only students who have completed at least 7 courses at the end of the second semester will be permitted to register for the M.Tech Project Phase-I in the third semester. Students with more than one uncompleted course must complete all coursework by the end of the third semester and then register for M.Tech project credits, in consultation with Faculty Advisor and the Head of the Department. These students will take more than 4 semesters to meet the requirements for M.Tech degree.
- Maximum of two non-CSE department courses can be credited as Department electives, with the approval of the Faculty Advisor and HoD, CSE. The current list of non-CSE department courses approved by the department is given in Annexure-I. This list may be revised from time to time.
Annexure: List of non-CSE department courses that can be taken as department electives
| Sl No. | Course No. | Course Title |
| 1 | BT6270 | Computational Neuroscience |
| 2 | BT5420 | Computer Simulations of Biomolecular Systems |
| 3 | EE5120 | Applied Linear Algebra |
| 4 | EE5121 | Convex Optimization |
| 5 | EE5130 | Digital Signal Processing |
| 6 | EE5140 | Digital Modulation and Coding |
| 7 | EE5142 | Introduction to Information Theory and Coding |
| 8 | EE5154 | Complex Network Analytics |
| 9 | EE5162 | Information Theory |
| 10 | EE5170 | Speech Signal Processing |
| 11 | EE5175 | Image Signal Processing |
| 12 | EE5176 | Computational Photography |
| 13 | EE6132 | Machine Learning for Computer Vision |
| 14 | MA5011 | Advanced Graph Theory |
| 15 | MA5014 | Applied Stochastic Processes |
| 16 | MA5015 | Number Theory |
| 17 | MA5440 | Combinatorics and Number Theory |
| 18 | MA5850 | Operations Research |
| 19 | MA5890 | Numerical Linear Algebra |
| 20 | MA6001 | Introduction to Coding Theory |
| 21 | MA6005 | Applied Linear Algebra |
| 22 | MA6190 | Mathematical Logic |
| 23 | MA6210 | Combinatorial Optimization |
| 24 | MA6312 | Mathematical theory of Games |
| 25 | MA6420 | Algebraic Theory of Codes and Automata |
| 26 | MA6470 | Commutative algebra |
| 27 | MA6480 | Galois theory |
