Carnegie Mellon University

PhD in Machine Learning

Course 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 (Previously Data Analysis)
    *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
  • 15-750 Algorithms or 15-853 Algorithms in the Real World
  • 15-780 Graduate Artificial Intelligence
  • 15-826 Multimedia Databases and Data Mining
  • 36-707 Regression Analysis
  • 36-709 Advanced Statistical Theory I
  • 36-710 Advanced Statistical Theory II
  • 36-752 Advanced Probability Overview

*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.)

Some students will have taken similar courses at other universities before entering the MLD PhD 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 advisor, and will be evaluated by the PhD Director. 

Fall - Year 1

Spring - Year 1
10-715 Adv. Intro to Machine Learning 10-716 Adv. Machine Learning: Theory & Methods
36-705 Intermediate Statistics Core or Menu course
10-920 Reading & Research 10-920 Reading & Research
Fall - Year 2 Spring - Year 2
Core or Menu Course  Elective Course* or Menu Core
Elective Course* or Menu Core 10-920 Reading & Research
10-920 Reading & Research MILESTONE - Speaking Skills should be completed
*Depending on when your chosen elective course is offered, take in either fall or spring.
Fall - Year 3 Spring - Year 3
Complete 1st TA Requirement Complete 2nd TA Requirement
10-920 Reading & Research 10-920 Reading & Research
MILESTONE - Courses Complete MILESTONE - TA Requirement Complete by end of semester
MILESTONE - Check to see if you have completed the MS in ML-Research degree on the way to your PhD
Fall - Year 4 Spring - Year 4
Thesis Proposal 10-930 Dissertation Research
10-920 Reading & Research
MILESTONE - Writing Skills should be complete
Fall - Year 5 Spring - Year 5
10-930 Dissertation Research 10-930 Dissertation Research
Thesis Defense
MILESTONE - GRADUATE!