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 4 out of 9 courses before applying:
- 10-701 or 10-715 Introduction to Machine Learning
- 36-700 or 36-705 Statistics
- 1 other course from the Set Core or Menu Core
- 1 other course from the Set Core, Menu Core, or Electives
- Must have the approval of their advisor (PhD students) or immediate supervisor (faculty and staff).
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. Since Secondary MS students complete the same curriculum as Primary MS students, this means Secondary MS students are often in the program for longer.
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.
While the Primary MS only has admissions once a year in Fall, applications to the Secondary MS are accepted in both Fall and Spring. An applicant who is not admitted to the Secondary MS is allowed to apply to the Secondary MS one more time in a future semester.
International students should be aware that the Secondary MS does not qualify them for CPT/OPT.
- Before applying: 10-701 or 10-715 Introduction to Machine Learning, 36-700 or 36-705 Statistics, 1 Set Core or Menu Core course, and 1 Set Core, Menu Core, or Elective course.
- After admittance: Many Secondary MS students take 1 or 2 courses per semester and so finish the program in 1-2 years, but other schedules are possible.
Recommendations to Prospective Applicants
Applicants are encouraged to apply as soon as they have completed the required 4 courses, and at least one full semester before they intend to graduate.
Admittance to the Secondary MS grants priority access to Machine Learning Department courses, and some courses (like 10-718 Data Analysis) may not have room for anyone who is not a Machine Learning student.
Please note that we continually review our program requirements and update them as necessary to ensure an appropriate and up-to-date curriculum. The program requirements applicable to you would be those in effect when your admission offer is made, which are not necessarily those in effect today.
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)
- 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.
- Approval from PhD advisor or immediate supervisor, sent directly to the Program Coordinator
The Statement of Purpose should include:
- Your objective in pursuing a Master's degree in Machine Learning
- Your background in fields particularly relevant to your objective, including any relevant academic or industrial experience
- Any additional information you wish to supply to the Admissions Committee
The approval statement from the advisor or supervisor may be as simple as, "I support [applicant]'s decision to pursue the Secondary MS in Machine Learning."
All materials must be received by the application deadline for consideration that semester. The Program Coordinator will store any materials submitted early until the application deadline.
The preferred method of submission is to email all materials to the Program Coordinator Dorothy Holland-Minkley at firstname.lastname@example.org. Mailing or delivering them to GHC 8008 is also acceptable if necessary.
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. During Fall and Spring, you can stop by her prospective student office hours on Fridays, 2 PM - 3 PM, in GHC 8008. You can also email her at any time at email@example.com.