Primary Master's in Machine Learning
The primary 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 4 Set Core courses, 3 Menu Core courses, 2 electives, and a practicum.
Refer to the Machine Learning Master's Curriculum for full information.
A typical schedule for a student in the program might be:
- Fall semester, year 1: 10-701 or 10-715 Intro to Machine Learning + 36-700 or 36-705 Statistics + a Menu Core course.
- Spring semester, year 1: 10-716 New Statistical Machine Learning + 10-718 Data Analysis + a Menu Core course.
- Summer semester, year 1: Practicum (internship or research related to Machine Learning).
- Fall semester, year 2: A Menu Core course + 2 Elective courses.
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.
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.
Applications are accepted only once a year. All students begin the program in August, having applied the previous December.
Frequently Asked Questions
What are the prerequisites? Do I need an undergraduate degree in Computer Science? What test scores do I need?
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)
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.
Due to Coronavirus/COVID-19, are GRE scores required in 2020?
We understand that the GRE may be difficult to take for applicants in 2020. If you have taken it or want to take it, please include your scores in your application. Otherwise, please know that the lack of GRE results will not be held against you for admission to the MS in Machine Learning program.
Is it possible to complete the degree online?
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.
Is it possible to complete the degree part-time?
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).
Is it possible to apply or begin the program in Spring?
Can I transfer in from another university or from another program at CMU?
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.
I already have a master's degree. Can I still apply?
How does the Master's in Machine Learning compare with other programs at CMU?
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.
Is this a STEM program?
Where are your graduates working?
When should I apply? When will I hear back?
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. You can email her at any time at email@example.com.