Carnegie Mellon University

Master's in Machine Learning

The master's application is intended for applicants who are not currently at Carnegie Mellon University.


The curriculum for the Master's in Machine Learning requires 6 core courses, 3 electives, and a practicum.

Refer to the Machine Learning Master's Curriculum for full information.

Typical Schedule

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 + 36-700 or 36-705 Statistics + 1 elective course.
  2. Spring semester, year 1: 2 core courses + 1 elective course.
  3. Summer semester, year 1: Practicum (internship or research related to Machine Learning).
  4. Fall semester, year 2: 10-718 Machine Learning in Practice + 1 core course + 1 elective course.

As the schedule shows, the MS in Machine Learning can be completed in three semesters by a motivated and well-prepared student. However, some 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.


The Machine Learning Department uses the School of Computer Science (SCS) Graduate Online Application. You may apply for multiple programs at Carnegie Mellon and the Machine Learning Department's MS Admissions Committee will consider your application independently.

Applications are accepted only once a year. All students begin the program in August, having applied the previous December.

For application information, including application deadlines, please refer to the SCS Master's Admissions page and SCS Master's Admissions FAQ.

Frequently Asked Questions

We welcome applicants from a variety of backgrounds and an undergraduate degree in Computer Science is not required.

Incoming students must have a strong background in computer science, including a solid understanding of complexity theory and good programming skills, as well as a good background in mathematics. Specifically, the first-year courses assume at least one year of college-level probability and statistics, as well as matrix algebra and multivariate calculus.

For our introductory ML course, there's a self-assessment test [PDF] which will give you some idea about the background we expect students to have (for the MS you're looking at the "modest requirements"). Generally, you need to have some reasonable programming skills, with experience in Matlab/R/scipy-numpy especially helpful, and Java and Python being more useful than C, and a solid math background, especially in probability/statistics, linear algebra, and matrix and tensor calculus.

The average scores of accepted applicants for Fall 2019 were as follows:

Undergraduate Overall GPA: 3.9 / 4.0 or 9.6 / 10.0.

GRE Quantitative: 169 (95th percentile)
GRE Verbal: 161 (86th percentile)
GRE Analytical Writing: 4.4 (71st percentile)

TOEFL: 111

There was significant variation in all of these scores, and they are only a small portion of applicants' qualifications. We do take people with a range of backgrounds for the MS.

For information about our selectivity rate and other statistics, please refer to the comparison PDF of all master's programs offered by the School of Computer Science.

Yes, the GRE General Test will once again be required for applications to the Master's in Machine Learning programs.

We do not require or expect applicants to take a GRE Subject Test.

No; at this time, we are not offering online or distance-learning classes. You must be physically present in Pittsburgh and able to attend classes on-campus to complete the program.

Yes, you can study part-time as long as you are able to attend the classes.

International students should be aware that student visas require that students complete the program full-time and finish the program by the end of their 3rd semester (in December).

No; applications are accepted only in December and students must begin the program in August. We are not able to make exceptions to this due to the timing of our Set Core courses.

No; you may not simply transfer into our program. You must submit an application and be accepted into the program, following the same application procedure as other applicants. Furthermore, the Machine Learning program does not accept transfer credit from other universities, although in certain situations a specific course requirement may be waived and an additional elective may be taken in its place.

Current CMU undergraduates may be able to apply for the 5th-Year Master's, which begins immediately after they have completed their bachelor's.

Yes, we welcome applicants from all backgrounds. As with all applicants, please make sure that your statement of purpose makes it clear why you believe an additional master's will help you achieve your goals.

Carnegie Mellon has compiled a comparison of its Master's Programs in Data Science.

The School of Computer Science has also compiled a comparison of all master's programs offered by SCS, including a PDF comparing program outcomes, average applicant scores, and selectivity rates.

Yes. As a program in Carnegie Mellon's School of Computer Science, the Master's in Machine Learning is a STEM program.
The Career and Professional Development Center compiles Post-Graduate Salaries & Destination Information about all graduates. You can also see where all of our alumni are working by going to the Machine Learning Alumni webpage.
The application deadline can be found on the SCS Master's Admissions page; it changes from year to year but is generally in late November or early December. You should expect an email response sometime in February. If you apply to multiple programs, you should expect to receive separate responses from each.

For questions about the Machine Learning Master's Program that have not been answered on our webpages, please contact the Master's Programs Admissions Coordinator, Laura Winter. You can email her at any time at .