PhD Program in Machine Learning
Carnegie Mellon University's doctoral program in Machine Learning is designed to train students to become tomorrow's leaders through a combination of interdisciplinary coursework, hands-on applications, and cutting-edge research. Graduates of the Ph.D. program in Machine Learning will be uniquely positioned to pioneer new developments in the field, and to be leaders in both industry and academia.
Understanding the most effective ways of using the vast amounts of data that are now being stored is a significant challenge to society, and therefore to science and technology, as it seeks to obtain a return on the huge investment that is being made in computerization and data collection. Advances in the development of automated techniques for data analysis and decision making requires interdisciplinary work in areas such as machine learning algorithms and foundations, statistics, complexity theory, optimization, data mining, etc.
The Ph.D. Program in Machine Learning is for students who are interested in research in Machine Learning.
Machine Learning is committed to providing full tuition and stipend support for the academic year, for each full-time ML Ph.D. student, for a period of 5 years. Research opportunities are constrained by funding availability. ML's funding commitments assume that the student is making satisfactory progress in the program, as reported to the student at the end of each academic term. Students are strongly encouraged to compete for outside fellowships and other sources of financial support. ML will supplement these outside awards in order to fulfill its obligations for tuition and stipend support.
PhD Program Requirements
PhD Program Requirements
- Completion of required courses, Core Courses + 1 Elective
- Mastery of proficiencies in Teaching, Conference Presentation, and Research skills.
- Successful defense of a Ph.D. thesis.
Note: Some students will have taken some of the above courses before entering the MLD PhD program: for example, as MS students at CMU. If students have previously taken the above-named courses at Carnegie Mellon before joining the MLD PhD, those may be used to satisfy the requirements and do not need to be repeated. (Note that courses can only be used for a single Master's degree.)
Some students will have taken similar courses at other universities before entering the MLD PhD program. Based on such equivalent coursework, any student can apply to replace (not reduce) up to two courses with either menu cores or electives. All requests must be supported by the advisor, and will be evaluated by the PhD co-directors.
Rules for the MLD PhD Thesis Committee (applicable to all ML PhDs):
The committee should be assembled by the student and their advisor, and approved by the PhD Program Director(s). It must include:
- At least one MLD Core Faculty member
- At least one additional MLD Core or Affiliated Faculty member
- At least one External Member, usually meaning external to CMU
- A total of at least four members, including the advisor who is the committee chair
Conference Presentation Skills
During their second or third year, Ph.D. students must give a talk at least 30 minutes long, and invite members of the Presentation Skills committee to attend and evaluate it. As preparation for this requirement, students are invited to attend optional workshops on presentation skills, which will be offered twice in each semester (or more often, by demand).
Ph.D. students are required to serve as Teaching Assistants for two semesters in Machine Learning courses (10-xxx), beginning in their second year. This fulfills their Teaching Skills requirement.
It is expected that all Ph.D. students engage in active research from their first semester. Moreover, advisor selection occurs in the first month of entering the Ph.D. program, with the option to change at a later time. Roughly half of a student's time should be allocated to research and lab work, and half to courses until these are completed.
In addition, students must follow all university policies and procedures.
Here is a typical schedule for the first two years of study:
FALL - 1st Year
SPRING - 1st Year
|10-715 Adv. Machine Learning||10-716 Statistical Machine Learning|
|36-705 Intermediate Statistics||10-718 Data Analysis Course (DAC)|
|10-920 Research (24 units)||10-920 Research (24 units)|
FALL - 2nd Year
SPRING - 2nd Year
|Core course or Elective||Core course or Elective|
|Core course or Elective||10-920 Research (24 units)|
|10-920 Research (24 units)||Additional Elective for the MS Degree|
To earn the MS from MLD on the way to the PhD the student must satisfy all relevant requirements for the ML MS.
The First & Second Year
A typical full-time, graduate course load during the first two years consists each term of two classes (at 12 graduate units per class) plus 24 units of advanced research. A student has the opportunity to take several elective classes in addition to the required courses.
The Third Year
By the third year, a Ph.D. student should have completed all coursework. For those students seeking an academic position after completing the ML PhD, or those pursuing certain subfields, additional advanced electives in the allied disciplines of SCS, MCS, Philosophy, the Tepper School of Business, or others may be chosen in consultation with the student's advisor. As in each of the first two years, any coursework is supplemented by research, for a total of 48 units/term.
The Fourth Year and Beyond
A Ph.D. student typically presents a thesis proposal no later than the start of the fourth year, and then spends the fourth and sometimes fifth year working on their thesis research
GRE General test scores are required for the application. You must speak English well: if you are not a native speaker, we recommend a combined TOEFL score of 100, with no subscore below 25, although we will make exceptions to this cutoff in exceptional cases. Unofficially, we recommend a high level of comfort with math (particularly linear algebra, probability, and proofs) and computer programming (at the level of an undergraduate degree in computer science, although many of our applicants get the necessary experience without majoring in CS). It is possible to fill in some of this background on the fly, but you will be working hard to do so! In addition, the program is very competitive, so successful applications always stand out in some way from their peers -- for example grades, research experience, or recommendation letters.