PhD Program in Machine Learning
The Ph.D. Program in Machine Learning is for students who are interested in research in Machine Learning and Computational Statistics. The program is operated jointly by faculty in the School of Computer Science and Department of Statistics.
The extraordinary spread of computers and online data is changing forever the way that important decisions are made in many organizations. Hospitals now analyze online medical records to decide which treatments to apply to future patients, banks analyze past financial records to learn to spot future fraud, and factories analyze past operations to learn to produce higher quality goods. Scientific research in many fields, notably the biological sciences, is also undergoing significant change as a result of dramatic increases in online data.
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, the statistical and computational principles that underly these algorithms, database and data warehousing methods, complexity analysis, data visualization, privacy and security issues, and application areas such as business, marketing, and public policy.
Carnegie Mellon University's doctoral program in Machine Learning is designed to train students to become tomorrow's leaders in this rapidly growing area. The program is part of CMU's Machine Learning Department which is made up of a multi-disciplinary team of faculty and students across several academic departments. Machine Learning is dedicated to furthering scientific understanding of automated learning and to producing the next generation of tools for data analysis and decision making based on that understanding.
Today's demand for expertise in machine learning far exceeds the supply, and this imbalance will become more severe over the coming decade. 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 this field, and to be leaders in both industry and academia.
Overview of Ph.D. Program Requirements
Completion of required courses, Data Analysis project and MS degree within 3 years but many students do it in 2 or 2.5 years
Mastery of proficiencies in Programming; Teaching; Conference Presentation and Research skills
Successful defense of a Ph.D. thesis
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. In this situation 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. Students who took 10-701 in Spring 2014 or earlier can use it as a core course, even if they weren't part of the MLD PhD program at the time they took 10-701.
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 during the first three years of study.
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 & Discovery, 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
- 10-705 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 Adv. 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||Core course or 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 Tepper School of Business. 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.
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.
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.
The only official prerequisite is that 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.