PhD Curriculum
| SEMESTER | COURSE CODE | NAME OF THE COURSE | CREDIT |
|---|---|---|---|
| 1 | AI 5000 | Foundations of Machine Learning | 3 |
| 1 | AI 5030 | Probability and Stochastic Processes | 3 |
| 1 | AI 5100 | Deep Learning | 3 |
| 1 | EE 5609 | Matrix Theory | 3 |
| 1 | CS 6013 /ID2230 | Advanced Data Structures and Algorithms / Data Structures & Applications | 3 |
| 1 | AI Electives | 9 |
| NAME OF THE COURSE | CREDIT |
|---|---|
| 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 |
| SEMESTER | COURSE CODE | NAME OF THE COURSE | CREDIT |
|---|---|---|---|
| 1 | AI 5000 | Foundations of Machine Learning | 3 |
| 1 | CS 6013 /ID2230 | Advanced Data Structures and Algorithms / Data Structures & Applications | 3 |
| 1 | Electives | 6 |
| NAME OF THE COURSE | CREDIT |
|---|---|
| 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 |