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 2513:1Data Structures and Algorithms
E1 2223:0Stochastic Models and Applications
 E2 2023:0Random Processes
E0 2983:1 Linear Algebra and Its Applications
E0 2303:1Computational Methods of Optimization
E1 2133:1Pattern Recognition and Neural Networks
E0 2703:1Machine Learning
E2 2363:1Foundations of Machine Learning

Pool-B courses

E1 2773:1Reinforcement Learning
E1 2163:1Computer Vision
E9 2412:1Digital Image Processing
E9 2613:1Speech Information Processing
E1 2543:1Game Theory
E1 2413:0Dynamics of Linear Systems
E0 2593:1Data Analytics
E2 2313:0Topics in Statistical Methods
E9 208  3:1  Digital Video: Perception and Algorithms  


AI 2990:21Dissertation 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 2653:1Convex Optimization and Applications
E0 3343:1Deep Learning for Natural Language Processing
E0 2683:1Practical Data Science
DS 2563:1Scalable Systems for Data Science
E9 2053:1Machine Learning for Signal Processing
DS 2223:1Machine Learning with Large Data sets
DS 2653:1Deep Learning for Computer Vision
E0 3063:1Deep Learning: Theory and Practice
E0 2493:1Approximation Algorithms
E0 2353:1Cryptography
E0 2383:1Intelligent Agents
E2 2013:0Information Theory
E1 2453:0Online Prediction and Learning
E2 2073:0Concentration Inequalities
E1 2443:0Detection and Estimation Theory
E1 3963:0Topics in Stochastic Approximation Algorithms
E2 2303:0Network Science and Modelling
E1 2463:1Natural Language Understanding
E9 2533:0Neural Networks and Learning Systems
E9 3093:1Advanced Deep Learning
CPS 3132:1Autonomous 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)