Carnegie Mellon University

Primary Master's in Machine Learning

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

Curriculum

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.

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 + 36-705 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.

Finances

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.

Apply

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

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

Frequently Asked Questions

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 2017 were as follows:

Undergraduate Overall GPA: 3.9 / 4.0, or 9.6 / 10.0, or 92 / 100.

GRE Quantitative: 169 (96th percentile)
GRE Verbal: 161 (84th percentile)
GRE Analytical Writing: 4.0 (59th percentile)

TOEFL: 106

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.

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.

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, and current CMU PhD students may apply for the Secondary Master's, which they can earn while pursuing their original PhD.

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.

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.

For questions about the Machine Learning Master's Program that have not been answered on our webpages, please contact the Machine Learning Master's Programs Coordinator, Dorothy Holland-Minkley. Stop by her prospective student office hours on Fridays, 2 PM - 3 PM, in GHC 8001, or email her at dfh@cs.cmu.edu.