Carnegie Mellon University

Secondary Master's in Machine Learning

The Secondary Master's in Machine Learning allows current CMU PhD students, faculty, and staff to complete the MS program at their own pace and often with minimal financial burden.

Requirements for Consideration

  • Must be a current PhD student, faculty member, or staff member at CMU.
  • Must take 10-701 or 10-715 Introduction to Machine Learning and 36-705 Intermediate Statistics and earn at least a B+ in both courses before applying.
  • Must have the approval/nomination of their advisor (PhD students) or immediate supervisor (faculty and staff).
  • Must have the endorsement of a prospective Data Analysis Project advisor.

Differences Between the Standard and Secondary MS

Since Secondary MS students are already at CMU for another purpose, the Secondary MS is generally completed part-time and so usually takes longer than 3 or 4 semesters.

The curriculum for the Secondary MS is the same as the standard MS. The exception is that students pursuing a PhD in Statistics may petition to have their Advanced Data Analysis (ADA) project count to satisfy the Data Analysis Project (DAP).

For PhD students, the practicum may be fulfilled by Reading & Research taken in your home department, if such research contains significant machine learning content. Furthermore, if a course is counted towards your PhD degree, it may also be counted for the Secondary MS, so long as such double-counting is permitted by your PhD program. However, any course counted towards another master's-level or bachelor's-level degree may not be counted toward the Secondary MS.

The tuition and fees of the Secondary MS are same as with the standard MS. However, most PhD students have their full-time tuition paid by their home department and so are not charged anything extra for the Secondary MS. Similarly, most faculty and staff are eligible for tuition benefits and so can avoid significant tuition charges by completing the program at an appropriate rate. However, the Machine Learning Department does not provide financial assistance, and so Secondary MS students should be prepared to pay any tuition and fees that aren't covered by other sources.

International students should be aware that the Secondary MS does not qualify them for CPT/OPT.

Typical Schedule

  1. Before applying: 10-701 or 10-715 Introduction to Machine Learning and 36-705 Intermediate Statistics
  2. After admittance: Many Secondary MS students take 1 or 2 courses per semester and so finish the program in 2-4 years, but other schedules are possible.

Recommendations to Students

Applicants are encouraged to apply as soon as they have completed 10-701 or 10-715 Introduction to Machine Learning and 36-705 Intermediate Statistics. In any case, applications must be submitted before beginning the DAP to ensure that the project is sufficiently related to Machine Learning.

To gain the endorsement of a prospective DAP advisor, applicants should review the research of Machine Learning Core Faculty and Affiliated Faculty. Applicants should then meet with faculty whose interests match their own and discuss potential DAP topics. When the applicant 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.

How to Apply

The Fall application deadline is October 12 and the spring application deadline is March 22.

The application materials are:

  • Application form (PDF)
  • Statement of Purpose (1 page)
  • Research Statement (1 page), describing the proposed DAP
  • Resume or CV
  • CMU transcript or Unofficial Academic Record
  • Test scores (GRE and TOEFL) from your prior CMU application, if applicable. These can usually be obtained from your home department if you no longer have a copy.
  • Nomination from PhD advisor or immediate supervisor, sent directly to the Program Coordinator
  • Endorsement from prospective DAP advisor, sent directly to the Program Coordinator

The Statement of Purpose should include:

  1. Your objective in pursuing a Master's degree in Machine Learning
  2. Your background in fields particularly relevant to your objective, including any relevant academic or industrial experience
  3. Any additional information you wish to supply to the Admissions Committee

The nomination from the advisor or supervisor may be as simple as, "I support [applicant]'s decision to pursue the Secondary MS in Machine Learning."

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."

All materials, including endorsement and nomination letters, must be received by the application deadline for consideration that semester. The Program Coordinator will store any materials submitted early until the application deadline.

Email or deliver all materials to:
Dorothy Holland-Minkley (
Master's Programs Coordinator
GHC 8001
Machine Learning Department
Carnegie Mellon University

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