PhD New Curriculum
In order to encourage breadth and increase flexibility for our PhD students as of Fall 2025, this is the new PhD curriculum.
Required Courses
(Should be taken in the first semester since both are only offered in the fall semester)
10-715 Advanced Introduction to Machine Learning
36-705 Intermediate Statistics for PhD
Menu Courses
PhD Students take one course from each category:
One Theory course: mathematical foundations and proofs
One Methods course: algorithms and implementation
One Practice course: application and aspects of ML in practice
Categories for Menu Courses:
Theory (choose one)
10-708 Probabilistic Graphical Models
10-716 Advanced ML: Theory and Methods
10-725 Optimization for Machine Learning
10-734 Foundations of Autonomous Decision Making under Uncertainty
36-709 Advanced Statistical Theory I
36-710 Advanced Statistical Theory II
Methods (choose one)
10-723 Generative AI
10-703 Deep Reinforcement Learning & Control
10-707 Advanced Deep Learning
10-714 Deep Learning Systems
15-750 Algorithms in the Real World
15-850 Advanced Algorithms
15-780 Graduate Artificial Intelligence
36-707 Regression Analysis
Practice (choose one)
10-718 ML in Practice
10-805 ML with Large Datasets
MLD PhD students must take two electives, while in the program, which may be any course at the 700 or higher level in the School of Computer Science or Department of Statistics and Data Science (36-xxx), including additional courses from the Menu Core, or other courses by approval. The elective is chosen in consultation with the student's advisor. Courses outside SCS or Statistics and Data Science must have approval from the student's Advisor. Have your advisor send the approval to the PhD Program Manager.
Special Notes for Joint Ph.D. Programs:
Statistics and Machine Learning Joint Ph.D. Program
- Students must choose from the menu core courses with a prefix in a department that is not their home department.
- Students must take one elective, while in the program, which may be any ML course or Menu Course at the 700 or higher level. The second elective may be satisfied within the student's home department.
- Students may satisfy the Practice course requirement through the Advanced Data Analysis project in Statistics.
Neural Computation and Machine Learning Joint Ph.D. Program
- Students may satisfy the Practice course requirement by completing a data-intensive project for their second year milestone.
- Students must take one elective, while in the program, which may be any ML course or Menu Course at the 700 or higher level. The second elective may be satisfied within the student's home department.
Heinz and Machine Learning Joint Ph.D. Program
- Students complete the Practice course requirement in their program.
- Students must take one elective, while in the program, which may be any ML course or Menu Course at the 700 or higher level. The second elective may be satisfied within the student's home department.