Carnegie Mellon University

Master of Science in Machine Learning Curriculum

The MS in Machine Learning offers students with a Bachelor's degree the opportunity to improve their training with advanced study in Machine Learning. Incoming students should have good analytic skills and a strong aptitude for mathematics, statistics, and programming.

The program consists primarily of coursework, with a very limited research component.


The curriculum for the Master's in Machine Learning requires 3 Set Core courses, 2 Menu Core courses, 2 electives, a Data Analysis Project, and a practicum.

Set Core

MS students take all three Set Core courses:

  • 10-701 Introduction to Machine Learning or 10-715 Advanced Introduction to Machine Learning
  • 10-702 Statistical Machine Learning
  • 36-705 Intermediate Statistics

Menu Core

Students take their choice of two Menu 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
  • 36-752 Advanced Probability

Note: The two Menu Core courses must be taken from separate lines. E.g., a student may not use both 15-750 Algorithms and 15-853 Algorithms in the Real World to satisfy their Menu Core requirements.


Students take two electives, which can be any 12-unit course from the School of Computer Science or Department of Statistics & Data Science at the 700-level or above, including additional courses from the Menu Core. Additional examples of courses of interest can be found on the Master's Electives page.

Data Analysis Project (DAP)

The Data Analysis Project gives students an opportunity to do research with a Machine Learning faculty member using real-world data. It is conducted over two semesters, starting with 10-821 DAP Preparation (6 units) and finishing with 10-611 MS DAP Research (12 units). The Data Analysis Project culminates with a written report and an oral presentation at DAP Day.


MS students also complete a practicum (an internship or research related to Machine Learning), generally conducted during the summer.