Ph.D. Curriculum
Core Requirements
The curriculum for the Machine Learning Ph.D. is built on a foundation of six core courses and one elective.
A typical full-time Ph.D. student course load during the first two years consists of two classes each term (at 12 units per class) plus 24 units of research. It is expected that all Ph.D. students engage in active research from their first semester. Roughly half of a student's time should be allocated to research and half to courses until the courses are completed.
Required Core Courses
- 10-715: Advanced Introduction to Machine Learning
- 10-716: Advanced Machine Learning — Theory and Methods
- 36-705: Intermediate Statistics
- 10-718: Machine Learning in Practice*
*Students who are in the joint Ph.D. Program in Machine Learning and Statistics may satisfy this requirement through the ADA project in Statistics. Students in the joint Machine Learning-CNBC program may satisfy it by completing a data-intensive project for their second year milestone. Students in the Machine Learning-Heinz joint Ph.D. also complete it in their program.
Plus Any Two of the Following Menu Core* Courses
- 10-703: Deep Reinforcement Learning or 10-707: Topics in Deep Learning
- 10-708: Probabilistic Graphical Models
- 10-725: Optimization for Machine Learning (formerly Convex Optimization)
- 10-734: Foundations of Autonomous Decision Making Under Uncertainty
- 10-805: Machine Learning With Large Datasets
- 15-750: Algorithms in the Real World or 15-850: Advanced Algorithms
- 15-780: Graduate Artificial Intelligence
- 36-707: Regression Analysis
- 36-709: Advanced Statistical Theory I
- 36-710: Advanced Statistical Theory II
*Students in the Statistics and Machine Learning Joint Ph.D. Program must choose two of the menu core courses with a prefix in a department that is not their home department. Thus, statistics joint students should choose two 10- and 15- prefix courses, and machine learning joint students should choose two 36- and 15- courses. Students accepted to the statistics and machine learning program before spring 2021 are grandfathered in and follow previous rules.
Plus One Elective
- An additional course from the menu core list above.
- Any course at the 700 or higher level in SCS or Statistics (36-xxx).
- Other 700 or higher level courses by approval.
Note: Some students will have taken some of the above courses before entering the MLD Ph.D. program. For example, as master's students at CMU. If students have previously taken the above-named courses at CMU before joining the MLD Ph.D. program, those courses may be used to satisfy the requirements and do not need to be repeated. (Courses can only be used for a single master's degree.)
Some students will have taken similar courses at other universities before entering the MLD Ph.D. program. Based on such equivalent coursework, any student can apply to replace (not reduce) up to two courses with either menu cores or electives. All requests must be supported by the adviser, and will be evaluated by the Ph.D. Director.