Ph.D. Curriculum
-
Electives
| Curriculum for Direct PhD | ||
|---|---|---|
| Course Code | Course Name | Credits |
| AI5000 | Foundations of Machine Learning | 3 |
| AI5030 | Probability and Stochastic Processes | 3 |
| AI5100 | Deep Learning | 3 |
| AI5110 | Linear Algebra and Applications | 3 |
| AI5120 | Topics in Optimization | 3 |
| AI Electives | 9 | |
| Total | 24 | |
- Direct PhD candidates need to complete 24 credits of coursework in 1 year with 15 credits of mandatory (departmental core) courses.
- Electives not in given list can be considered with approval of faculty advisor and DGPC.
- The above displayed curriculum is effective July 2026 onwards.
| Curriculum for Regular/External PhD | ||
|---|---|---|
| Course Code | Course Name | Credits |
| AI5000 | Foundations of Machine Learning | 3 |
| AI Electives | 9 | |
| Total | 12 | |
- External PhD candidates need to complete 12 credits of coursework in 1 year with 3 credits of mandatory (departmental core) courses.
- Electives not in the given list can be considered with approval of faculty advisor and DPGC.
- The above displayed curriculum is effective July 2026 onwards.
| Elective List | |
|---|---|
| Course Name | Credits |
| Intro to Statistical Learning Theory | 1 |
| Kernel Methods | 1 |
| Sequence Models | 1 |
| Brain and Neuroscience | 1 |
| Optimization Methods in Machine Learning / Convex Optimization | 3 |
| Bayesian Data Analysis | 2 |
| Nonlinear Control Techniques | 3 |
| Information Theory and Coding | 3 |
| Stochastic Processes for Machine Learning | 1 |
| Introduction to Submodular Functions | 1 |
| Artificial Intelligence | 2 |
| Natural Language Processing | 3 |
| Information Retrieval | 3 |
| Text Processing | 3 |
| Data Mining | 3 |
| Computer Vision | 3 |
| Speech Systems | 3 |
| Image and Video Processing | 3 |
| Surveillance Video Analytics, Visual Big Data Analytics, Video Content Analysis | 3 |
| Computer Vision for Autonomous Vehicle Technology | 3 |
| Parallel & Concurrent Programming | 3 |
| Distributed Computing | 3 |
| An Overview of Reinforcement Learning | 3 |
| Game Theory and Mechanism Design | 3 |
| Neuromorphic Artificial Intelligence | 3 |
| Explainability in Machine Learning | 3 |
| AI and Sensors | 3 |
| Mobile Robotics | 3 |
| Cybersecurity and AI | 2 |
| Stochastic Processes and Applications | 3 |
| Generative Artificial Intelligence | 3 |