M.Tech Curriculum
- M.Tech (2 Years)
- M.Tech R.A – July (3 Years)
- M.Tech R.A – January (3 Years)
- Elective (M. Tech)
| Curriculum for M.Tech (2 Years) | ||||||
|---|---|---|---|---|---|---|
| Year | Odd Semester | Credits | Even Semester | Credits | ||
| 1 | AI5030 | Probability and Stochastic Processes | 3 | AI5100 | Deep Learning | 3 |
| AI5000 | Foundations of Machine Learning | 3 | AI5016 | Industry Lecture series | 1 | |
| EE5609 | Matrix Theory | 3 | AI Electives | 9 | ||
| CS6013 /ID2230 | Data Structures & Applications* | 3 | ||||
| LA5180 | Communication Skills : Advanced | 1 | ||||
| Total | 13 | Total | 13 | |||
| Summer | AI6115 | Thesis Stage - I | 3 | |||
| Summer Total | 3 | |||||
| 2 | AI6215 | Thesis stage โ II | 9 | AI6315 | Thesis stage โ III | 12 |
| Total | 9 | Total | 12 | |||
*Either the Advanced or Regular version can be taken
| Category | Credits | Percentage |
|---|---|---|
| Department Core | 49 | 98.00% |
| LA/CA | 1 | 2.00% |
| Total | 50 | 100% |
| Curriculum for M.Tech R.A (July Admission) | ||||||
|---|---|---|---|---|---|---|
| Year | Odd Semester | Credits | Even Semester | Credits | ||
| 1 | AI5030 | Probability and Stochastic Processes | 3 | AI5100 | Deep Learning | 3 |
| AI5000 | Foundations of Machine Learning | 3 | AI5016 | Industry Lecture series | 1 | |
| EE5609 | Matrix Theory | 3 | AI Electives (See Below) | 6 | ||
| LA5180 | Communication Skills : Advanced | 1 | ||||
| Total | 10 | Total | 10 | |||
| 2 | AI6115 | Thesis Stage - I | 3 | AI6215 | Thesis stage โ II | 6 |
| CS6013 | Advanced Data Structures & Applications | 3 | ||||
| AI Electives (See Below) | 3 | |||||
| Total | 9 | Total | 6 | |||
| 3 | AI6315 | Thesis stage โ III | 6 | AI6415 | Thesis stage โ IV | 9 |
| Total | 6 | Total | 9 | |||
| Category | Credits | Percentage |
|---|---|---|
| Department Elective | 9 | 18.00% |
| Department Core | 40 | 80.00% |
| LA/CA | 1 | 2.00% |
| Total | 50 | 100% |
| Curriculum for M.Tech R.A (Jan Admission) | ||||||
|---|---|---|---|---|---|---|
| Year | Odd Semester | Credits | Even Semester | Credits | ||
| 1 | AI5030 | Probability and Stochastic Processes | 3 | AI5100 | Deep Learning | 3 |
| AI5000 | Foundations of Machine Learning | 3 | EE5609 | Matrix Theory | 3 | |
| AI Electives (See Below) | 3 | CS6013 | Advanced Data Structures & Applications | 3 | ||
| LA5180 | Communication Skills : Advanced | 1 | AI5016 | Industry Lecture series | 1 | |
| Total | 10 | Total | 10 | |||
| 2 | AI6115 | Thesis Stage - I | 3 | AI6215 | Thesis stage โ II | 6 |
| AI Electives (See Below) | 6 | |||||
| Total | 9 | Total | 6 | |||
| 3 | AI6315 | Thesis stage โ III | 6 | AI6415 | Thesis stage โ IV | 9 |
| Total | 6 | Total | 9 | |||
| Category | Credits | Percentage |
|---|---|---|
| Department Elective | 9 | 18.00% |
| Department Core | 40 | 80.00% |
| LA/CA | 1 | 2.00% |
| Total | 50 | 100% |
| Elective List for M.Tech | |
|---|---|
| Course Code | Course Name |
| AI3102 | Sequence models |
| AI5040 | Game Theory and Mechanism Design |
| AI5120 | Explainability in ML |
| AI5153 | Mobile Robotics |
| AI5133 | AI and sensors |
| AI5090 | Stochastic Processes and Applications |
| AI5073 | Neuromorphic Artificial Intelligence |
| AI3603/CS5290 | Computer Vision |
| Reinforcement Learning | |
| Convex Optimization | |
| Natural Language Processing | |
| Artificial Intelligence | |
| Generative Artificial Intelligence | |
| CS6170 | Computer Vision for Autonomous Vehicle Technology |
| CS5300 | Parallel & Concurrent Programming |
| CS5350 | Bayesian Data Analysis |
| CS6370 | Information Retrieval |
| CS5700 | Text processing and Retrieval |
| CS6870 | Surveillance Video Analytics, Visual Big data analytics, Video content analysis |
| CS6140 | Video Content Analysis |
| CS5600 | Data Mining |
| CS6460 | Visual Big Data Analytics |
| CS5320 | Distributed Computing |
| CS6430 | Stochastic Processes in Machine Learning |
| HT5030 | Brain & Neuroscience |
| EE5604 | Intro to Statistical Learning theory |
| EE5605 | Kernel Methods for ML |
| EE5470 | Nonlinear Control Techniques |
| EE5903 | Information Theory, Coding and Inference |
| EE5328 | Introduction to Submodular Functions |
| EE6307 | Speech Systems |

