Primary Master's in Machine Learning - Applied Study
The primary master's application is intended for applicants who are not currently at Carnegie Mellon University. The Master's in Machine Learning - Applied Study is only available in the primary master's format.
The curriculum for the Master's in Machine Learning - Applied Study requires 7 Core courses, 2 electives, an internship, and professional development. The coursework component is the same between different versions of the master's.
Refer to the Machine Learning Master's Curriculum for full information.
A typical schedule for a student in the program might be:
- Summer before year 1: Preparation for internship search
- Fall semester, year 1: 10-701 Intro to Machine Learning + 36-700 Statistics + a Core course + professional development + internship search
- Spring semester, year 1: 10-716 Advanced Machine Learning + a Core course + an elective + professional development + internship search
- Summer semester, year 1: Internship related to machine learning
- Fall semester, year 2: 10-718 Data Analysis + a Core course + an elective + professional development + job search
Students engage each semester in professional development activities such as resume and interview prep workshops, practicing effective communication and teamwork skills, and networking. The required summer internship gives students an opportunity to work with real-world data in an industry setting, which presents different challenges than when completing coursework.
As the schedule shows, the MS in Machine Learning - Applied Study can be completed in three semesters by a motivated and well-prepared student.
The MS in Machine Learning - Applied Study 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 Master's 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 MS in Machine Learning - Applied Study is new as of 2020. However, the average scores of accepted applicants for the MS in Machine Learning 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.
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, which are generally held during the day on weekdays.
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 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 in Machine Learning, which begins immediately after they have completed their bachelor's, and current CMU PhD students may apply for the Secondary Master's in Machine Learning, which they can earn while pursuing their original PhD. However, note that the Master's in Machine Learning - Applied Study is not available as a 5th-Year or Secondary Master's.
I already have a master's degree. Can I still apply?
How does the Master's in Machine Learning - Applied Study 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.
The Master's in Machine Learning - Applied Study is similar to the Master's in Machine Learning. The Applied Study degree is ideal for students who are interested in a career in industry with students regularly engaging in professional development outside the classroom and getting experience working with industry employers during the summer. In comparison, the Master's in Machine Learning allows students to spend more time on research within an academic environment.
Where are your graduates working?
When should I apply? When will I hear back?
Where can I find more information about the program?
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.