M.Tech. (AI) Curriculum

The curriculum of the two-year M.Tech. (AI) programme comprises a total of 64 credits of which 43 credits account for course-work and 21 credits for project work. The course-work is organized as follows:

  • Pool-A courses (Hardcore): 19 credits
  • Pool-B courses (Softcore): Minimum 12 credits
  • Pool-C courses (Electives): Minimum credits to make a total of 43 credits of course-work

Pool-A courses

E0 251 3:1 Data Structures and Algorithms
E1 222 3:0 Stochastic Models and Applications
(or)
 E2 202 3:0 Random Processes
E0 298 3:1  Linear Algebra and Its Applications
E0 230 3:1 Computational Methods of Optimization
E1 213 3:1 Pattern Recognition and Neural Networks
(or)
E0 270 3:1 Machine Learning
(or)
E2 236 3:1 Foundations of Machine Learning

Pool-B courses

E1 277 3:1 Reinforcement Learning
E1 216 3:1 Computer Vision
E9 241 2:1 Digital Image Processing
E9 261 3:1 Speech Information Processing
E1 254 3:1 Game Theory
E1 241 3:0 Dynamics of Linear Systems
E0 259 3:1 Data Analytics
E2 231 3:0 Topics in Statistical Methods
E9 208 3:1 Digital Video: Perception and Algorithms
 Project

AI 299

0:21 Dissertation Project

Recommended Electives The recommended electives are listed below. Pool B courses could also be taken as electives. Courses not listed here could also be taken as electives with prior approval of the faculty advisor.

E0 265 3:1 Convex Optimization and Applications
E0 334 3:1 Deep Learning for Natural Language Processing
E0 268 3:1 Practical Data Science
DS 256 3:1 Scalable Systems for Data Science
E9 205 3:1 Machine Learning for Signal Processing
DS 222 3:1 Machine Learning with Large Data sets
DS 265 3:1 Deep Learning for Computer Vision
E0 306 3:1 Deep Learning: Theory and Practice
E0 249 3:1 Approximation Algorithms
E0 235 3:1 Cryptography
E0 238 3:1 Intelligent Agents
E2 201 3:0 Information Theory
E1 245 3:0 Online Prediction and Learning
E2 207 3:0 Concentration Inequalities
E1 244 3:0 Detection and Estimation Theory
E1 396 3:0 Topics in Stochastic Approximation Algorithms
E2 230 3:0 Network Science and Modelling
E1 246 3:1 Natural Language Understanding
E9 253 3:0 Neural Networks and Learning Systems
E9 309 3:1 Advanced Deep Learning
CPS 313 2:1 Autonomous Navigation

Note: Students are advised to pay attention to the designation of a course at the time of registration on SAP. For example, a course designated as a Pool-C course at the time of registration cannot be redesignated as a Pool-B course later on. Also, the course type can be changed only from RTP to non-RTP and from credit to audit, and not the other way round.

(Last updated: February 28, 2022)

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