Program Overview

PhD Program in Machine Learning

The PhD program in Machine Learning at Carnegie Mellon University School of Computer Science is designed to encourage breadth and increase flexibility for PhD students. As of Fall 2025, the program has a new curriculum.

Required Courses

The following courses are required and should be taken in the first semester:

• 10-715 Advanced Introduction to Machine Learning
• 36-705 Intermediate Statistics for PhD

Menu Courses

PhD students must take one course from each of the following categories:

• Theory: mathematical foundations and proofs
  • 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: algorithms and implementation
  • 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: application and aspects of ML in practice
  • 10-718 ML in Practice
  • 10-805 ML with Large Datasets

Electives

MLD PhD students must take two electives, 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.

Special Notes for Joint PhD Programs

• Statistics and Machine Learning Joint PhD 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, 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 PhD Program:
  • Students may satisfy the Practice course requirement by completing a data-intensive project for their second year milestone.
  • Students must take one elective, 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 PhD Program:
  • Students complete the Practice course requirement in their program.
  • Students must take one elective, 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.