Machine Learning Requirements
Total Credits
Completion of at least thirty credits at the graduate level (500 level and above), with no grade less than a C in any course and a minimum GPA of 3.000.
At least four of the courses must be drawn from the set of core courses:
| CS 541 | Artificial Intelligence | 3 |
| CS 559 | Machine Learning: Fundamentals and Applications | 3 |
| CS 560 | Statistical Machine Learning | 3 |
| CS 583 | Deep Learning | 3 |
| CS 584 | Natural Language Processing | 3 |
A total of at least seven of the above core courses and the following elective courses:
| CS 513 | Knowledge Discovery and Data Mining | 3 |
| CS 532 | 3D Computer Vision | 3 |
| CS 544 | Health Informatics | 3 |
| CS 556 | Mathematical Foundations of Machine Learning | 3 |
| CS 558 | Computer Vision | 3 |
| CS 582 | Causal Inference | 3 |
| CS 589 | Text Mining and Information Retrieval | 3 |
| CS 598 | Visual Information Retrieval | 3 |
| CS 609 | Data Management and Exploration on the Web | 3 |
| BIA 654 | Experimental Design II | 3 |
| BIA 660 | Web Mining | 3 |
| BIA 662 | Cognitive Computing | 3 |
| BIA 678 | Big Data Technologies | 3 |
| CPE 608 | Applied Modeling and Optimization | 3 |
| CPE 695 | Applied Machine Learning | 3 |
| FE 541 | Applied Statistics with Applications in Finance | 3 |
| MA 541 | Statistical Methods | 3 |
| MA 630 | Advanced Optimization Methods | 3 |
| MA 641 | Time Series Analysis I | 3 |
| MA 661 | Dynamic Programming and Reinforcement Learning | 3 |
| CS 800 | Special Problems in Computer Science (M.S.) | 1-6 |
| CS 900 | Thesis in Computer Science (M.S.) | 1-10 |
CS 800: With one of the program faculty members, up to 6 credits
CS 900: With one of the program faculty members, 5-10 credits
General Electives
Up to three general electives, which can be any graduate course.