Carnegie Mellon University

5th-Year Master's in Machine Learning

The 5th-Year Master's in Machine Learning allows CMU students to complete a MS in Machine Learning in one additional year by taking some of the required courses as an undergraduate. Interested students apply earlier in Senior year than the standard application deadline and receive the response earlier as well.

Requirements for Consideration

  • Must be graduating with a Bachelor's degree from Carnegie Mellon University.
  • Must take at least 3 of the MS courses during the undergraduate years. (These courses may also count towards the Bachelor's degree.)
  • Must take 10-701 or 10-715 no later than the semester they apply and earn at least a B+.
  • Must have the endorsement of a prospective Data Analysis Project advisor.

Differences Between the Standard and Fifth-Year MS

Since 5th-Year MS students have taken 3 courses for the MS during their undergraduate years, the 5th-year MS can be completed in 2 semesters instead of the standard 3 semesters.

The curriculum for the 5th-year MS is the same as the standard MS. The exception to this is that one 9- or 12-unit elective at the 400- or 500-level may be used if it's taken as an undergraduate and if its requirements are identical to a cross-listed graduate-level version of the course. 10-601 may also be used as an elective if it's taken before 10-701/10-715. Along with the suggested master's electives, some of the minor electives may be of interest.

Finally, 5th-year MS students must complete the program as full-time students (at least 36 units/semester).

Typical Schedule

  1. Courses taken in undergraduate program: 10-701 Introduction to Machine Learning + 2 electives.
  2. Summer between 4th and 5th years: Practicum (internship or research related to Machine Learning). 
  3. Fall semester: 36-705 Intermediate Statistics + a Menu Core course + 10-821 DAP Preparation (6 units) + 6 other units (possibly 10-611 MS DAP Research).
  4. Spring semester: 10-702 Statistical Machine Learning + a Menu Core course + 10-611 MS DAP Research (12 units).

Recommendations to Students

Students can take 10-701 or 10-715 as late as the semester they apply, but should aim to take it during Junior year. If you are taking it in the semester you apply, please let us know to use your midterm grade. If your program rounds to the nearest letter grade, so that B+ shows up as a B, please let us know in your application so that we can verify your grade.

To request that a 400- or 500-level course be allowed as an elective, provide documentation with your application that shows that it is identical to a cross-listed graduate-level course. Examples of proof include a syllabus that says the requirements are the same or an email from the course professor. If possible, however, students considering the 5th-year MS are encouraged to register for the graduate-level version of electives instead.

Applicants should be aware that only 3 courses may double-count between the Bachelor's and Master's degree. Students will not be forced to re-take courses, but may need to take additional electives if more than three core courses were taken before earning theit Bachelor's degree.

It is important to be able to start on the Data Analysis Project (DAP) as soon as the 5th year begins. Students may wish to consider working on related research during the senior year, during the practicum between the 4th and 5th years, or as extra units while taking 10-821 DAP Preparation during the first semester of the 5th year. Students who do not yet have research experience are particularly strongly encouraged to consider doing research during their senior year or the following summer.

To gain the endorsement of a prospective DAP advisor, students should review the research of Machine Learning Core Faculty and Affiliated Faculty. Students should then meet with faculty whose interests match their own and discuss potential DAP topics. When the student finds a faculty member who would like to advise them on a DAP that they would like to complete, they should write a one-page research proposal and send it to their prospective advisor for approval.

The 5th-year MS application does not require GRE scores or digital portfolios, and only requires one letter of recommendation instead of three. However, if a student is planning to apply to other graduate programs if not admitted to the 5th-year MS, in may be advisable to prepare for those other applications as normal.

The tuition and fees for the 5th-year MS are the same as the standard MS. 5th-Year MS students are also considered graduate students, not undergraduates, and so should contact Housing Services if they're living on-campus or their financial aid provider if they have one to learn what effects this may have.

How to Apply

The application deadline is October 12 of Senior Fall. Students who will be graduating a semester early and who want an earlier response can contact the Master's Programs Coordinator, Dorothy Holland-Minkley, to discuss the possibility of applying in Junior Spring.

The application materials are:

  • Application form (PDF)
  • Statement of Purpose (1 page)
  • Research Statement (1 page)
  • Resume or CV
  • CMU transcript or unofficial academic record
  • Documentation supporting a 400- or 500-level elective (if applicable)
  • Endorsement from prospective DAP advisor, sent directly to the Program Coordinator
  • One letter of recommendation, sent directly to the Program Coordinator

For the Statement of Purpose, type a one page concise statement including: your objective in pursuing a graduate degree in Machine Learning; your background in particularly relevant fields; any relevant academic or research experience; and any additional information you wish to supply to the Admissions Committee.

For the Research Statement, type a one page concise statement describing your proposed Data Analysis Project. This research statement should be approved by your proposed Data Analysis Project Advisor.

The endorsement from the prospective DAP advisor may be as simple as, "I have reviewed [applicant]'s research statement and am happy to supervise this DAP research." It should be emailed directly to Dorothy Holland-Minkley at dfh@cs.cmu.edu.

The letter of recommendation may be written by the prospective DAP advisor or by someone else who knows the applicant well. It should be emailed directly to Dorothy Holland-Minkley at dfh@cs.cmu.edu.

The applicant should email all other materials to Dorothy Holland-Minkley by October 12. Mailing or delivering them to GHC 8001 is also allowed, if preferred.

Additional Questions

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.