Carnegie Mellon University

Master of Science in Machine Learning

The MS in Machine Learning is part of CMU's Machine Learning Department, which is made up of a multi-disciplinary team of faculty and students across several academic departments. Machine learning is dedicated to furthering the scientific understanding of automated learning, and to producing the next generation of tools for data analysis and decision making based on that understanding.

Today's demand for expertise in machine learning far exceeds the supply, and this imbalance will become more severe over the coming decade. The MS program 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. An undergraduate degree in computer science is not required.

The program consists primarily of coursework, with a very limited research component. Students that complete the MS program are welcome to apply to the PhD program, but will not receive preferential treatment.


The curriculum for the Masters in Machine Learning requires five core machine learning courses, and two electives.

These Set Core courses together provide a foundation in machine learning, statistics, probability, and algorithms:

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

Students also take any two of the following 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

MS students are required to complete a Data Analysis Project (DAP), which consists of 10-821 DAP Preparation (6 units) and 10-611 MS DAP Research (12 units). The Data Analysis Project will be concluded by a written report and an oral presentation at DAP Day.

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

A typical schedule for a student in the program might be:

  1. Fall semester, year 1: 10-701 or 10-715 Intro to Machine Learning + 36-705 Intermediate Statistics + an elective.
  2. Spring semester, year 1: 10-702 Statistical Machine Learning + a Menu Core course + 10-821 DAP Preparation + an elective or research.
  3. Summer semester, year 1: Practicum (internship or research related to Machine Learning).
  4. Fall semester, year 2: A Menu Core course + 10-611 MS DAP Research + an elective or additional research.

As the schedule shows, the MS in Machine Learning can be completed in three semesters by a motivated and well-prepared student. However, many students finish in four semesters, spending the additional time on either research or filling in gaps in their undergraduate training.

The MS in Machine Learning program does not provide any financial support for this program and the student must pay tuition, student fees and living expenses on their own.

Please see the financial information webpage for costs.

Frequently Asked Questions about the ML Masters Program

Application deadlines & information

You must use the SCS Graduate Online Application to apply to the program.

For questions about the Machine Learning Masters Program that have not been answered on our webpages, please contact Dorothy Holland-Minkley (