PhD in Machine Learning
Course Requirements
The curriculum for the Machine Learning Ph.D. is built on a foundation of five core courses and three electives (plus the Data Analysis Project requirement). These five courses also comprise the required courses for the MS degree. Together with the Data Analysis Project requirement, these should be completed for MS degree within 3 years but many students do it in 2 or 2.5 years.
Note: If a student has taken some of the MLD core courses before joining the MLD PhD program, and has not counted these courses toward any other PhD-level degree, the student may count these courses toward the MLD PhD. For this purpose, students may count only identical courses taken at Carnegie Mellon University, not similar courses taken at Carnegie Mellon or other universities. If any of the MLD PhD core courses have been taken at Carnegie Mellon, the student will need to take fewer than 5 new core courses to graduate. A student must always take at least three elective courses while registered in the MLD PhD program, irrespective of any courses taken before joining the PhD program.
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. Thus, during the first two years, a student has the opportunity to take several elective classes in addition to the five required courses.
The ML curriculum joins courses with a Computer Science main theme and those with a Probability and Statistics main theme. These may be grouped, as follows:
In CS, relevant sub-fields include: Databases; Machine Learning, Data Mining and algorithms applications in areas such as Robotics, Information Retrieval and AI.
In Statistics (including Philosophy), the sub-fields include: Statistical modeling (e.g., hierarchical and times series); Bayes' Nets, Causation, and experimental design. The curriculum is based on core academic courses on Intermediate Statistics, Machine Learning, Statistical Machine Learning, Multimedia Databases, and Algorithms.
These core courses together provide a foundation in machine learning, statistics, probability, and algorithms.
- 10-715 Advanced Introduction to Machine Learning
- 10-702 Statistical Machine Learning
- 36-705 Intermediate Statistics
Plus any two of the following courses:
- 10-708 Probabilistic Graphical Models
- 10-725 Convex Optimization
- 15-826 Multimedia Databases and Data Mining
- 15-750 Algorithms or 15-853 Algorithms in the Real World
Data Analysis Project Requirement, in the second year, which serves in lieu of an MS thesis.
Here is a typical schedule for the first two years of study.
FALL - 1st Year |
SPRING - 1st Year |
10-715 Advanced Introduction to Machine Learning | 10-702 Statistical Machine Learning |
10-705 Intermediate Statistics | Core course or elective |
10-920 Research | 10-920 Research |
FALL - 2nd Year |
SPRING - 2nd Year |
Core course or Elective | Research for Data Analysis Project |
Elective | Elective |
10-920 Research | 10-920 Research |
The Data Analysis Project requirement:
During the second year a Ph.D. student is required to demonstrate data analysis and machine learning skills in the context of a focused project. The Data Analysis Project may be carried out either at Carnegie Mellon or at a sponsoring corporate institution under the joint supervision of the sponsor and a ML faculty. It will be concluded by a written report (in lieu of a Masters Thesis) in which the student demonstrates an ability to approach data mining problems in a way that cuts across existing disciplinary boundaries. The requirement includes a presentation in the ML Journal Club and also the submission of a DAP Paper. Passing this requirement will be the judgment of the DAP committee.
Student must form an official "DAP committee" of three faculty to evaluate the document. The committee will consist of the advisor, the Journal club instructor(s), and one other faculty member selected by the student. The third member is typically someone with an interest in the analysis of the data set, and does not have to be an expert in ML or part of the student's thesis committee.The student should form the committee as early as possible during the DAP research process, and inform Diane of who the members are. Two faculty from the committee are required to attend the presentation.
The Third Year
During the third year, a Ph.D. student completes the elective course requirements. One of these three electives is taken from the offerings in Statistics. The other two advanced electives, chosen in consultation with the students advisor, form a concentration in one of the allied disciplines with SCS, Biology, Philosophy, or GSIA. For those candidates seeking an academic position after completing the ML Ph.D. degree, the thoughtful selection of these three elective courses is particularly important. As in the each of the first two years, coursework is supplemented by 24 units/term of research.
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.
Research
Responsible Conduct of Research Training
Students must complete training for NSF and NIH grants. A copy of the certificate must be given to the MLD Business Manager, Colleen Everett.
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.
This Research page is a list of some of the projects for which ML faculty may be interested in recruiting students. Within each project there can be lines of research which range in size from a semester's work to an entire thesis (or beyond). So, this page is intended as a resource for students looking for a thesis advisor, for a Data Analysis project, or to collaborate for any other reason.