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, PhD student course load during the first two years consists each term of two classes (at 12 graduate 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 & Methods
• 36-705 Intermediate Statistics
• 10-718* Machine Learning in Practice
  *Students who are in the joint PhD program in ML & Statistics may satisfy this requirement through the ADA project in Statistics. Students in the joint ML-CNBC program may satisfy it by completing a data intensive project for their second year milestone. Students in the joint ML-Heinz joint PhD also complete it in their program.

Plus any 2 of the following Menu* of Core courses:

• 10-703 Deep Reinforcement Learning or 10-707 Topics in Deep Learning
• 10-708 Probabilistic Graphical Models
• 10-725 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 & ML joint program must choose two of the Menu of 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 & ML joint program before Spring 2021 are grandfathered and follow the previous rules.

Plus 1 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 PhD program: for example, as MS students at CMU. If students have previously taken the above-named courses at Carnegie Mellon before joining the MLD PhD, those may be used to satisfy the requirements and do not need to be repeated. (Note that courses can only be used for a single Master's degree.)