The curriculum of the two-year M.Tech. (AI) programme comprises a total of 64 credits of which 39 credits account for course-work and 25 credits for project work. The course-work is organized as follows:
- Pool-A courses (Hardcore): 19 credits
- Pool-B courses (Softcore): Minimum 12 credits
- Electives: Remaining credits to make a minimum total of 39 course credits
Pool A Courses:
| E0 251 | 3:1 | Data Structures and Algorithms |
| DS 215 | 3:0 | Introduction to Data Science |
| (or) | ||
| E1 222 | 3:0 | Stochastic Models and Applications |
| (or) | ||
| E2 202 | 3:0 | Random Processes |
| E0 230 | 3:1 | Computational Methods of Optimization |
| E0 298 | 3:1 | Linear Algebra and Its Applications |
| DS 216 | 3:1 | Machine Learning for Data Science |
| (or) | ||
| E0 270 | 3:1 | Machine Learning |
| (or) | ||
| E1 213 | 3:1 | Pattern Recognition and Neural Networks |
| (or) | ||
| E2 236 | 3:1 | Foundations of Machine Learning |
| (or) | ||
| E9 205 | 3:1 | Machine Learning for Signal Processing |
Pool B Courses:
| E0 259 | 3:1 | Data Analytics |
| E0 249 | 3:1 | Approximation Algorithms |
| E0 235 | 3:1 | Cryptography |
| E0 238 | 3:1 | Intelligent Agents |
| E0 271 | 3:1 | Graphics and Visualization |
| E1 277 | 3:1 | Reinforcement Learning |
| E1 216 | 3:1 | Computer Vision |
| E1 254 | 3:1 | Game Theory |
| E1 241 | 3:0 | Dynamics of Linear Systems |
| E1 245 | 3:0 | Online Prediction and Learning |
| E1 244 | 3:0 | Detection and Estimation Theory |
| E1 201 | 2:1 | Hardware Acceleration and Optimization for Machine Learning |
| E2 201 | 3:0 | Information Theory |
| E2 231 | 3:0 | Topics in Statistical Methods |
| E2 207 | 3:0 | Concentration Inequalities |
| E9 241 | 2:1 | Digital Image Processing |
| E9 261 | 3:1 | Speech Information Processing |
| E9 246 | 3:1 | Advanced Image Processing |
| E9 208 | 3:1 | Digital Video: Perception and Algorithms |
| CP 214 | 3:1 | Foundations of Robotics |
| CP 260 | 2:1 | Perception and Intelligence |
| DS 256 | 3:1 | Scalable Systems for Data Science |
| DS 265 | 3:1 | Deep Learning for Computer Vision |
| DS 207 | 3:1 | Introduction to Natural Language Processing |
Electives:
The remaining credits to make a minimum total of 39 course credits can be taken from among all courses offered in the institute with the approval of the advisor.
Project
AI 299A 0:25 Dissertation Project
(Last updated: 17 July, 2025)