Introduction

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 within 3 years
  
• Completion of the Data Analysis project and MS degree, within 2 years
  
• Mastery of proficiencies in Programming; Teaching; Conference Presentation and Research skills
  
• Successful defense of a Ph.D. thesis

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 during the first two 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 Approaches for Learning & Discovery, Multimedia Databases, and Algorithms.

The five core courses provide, respectively: a secure foundation in mathematical statistics, a survey of basic machine learning techniques with numerous applications; the statistical and probabilistic theoretical underpinnings for these techniques; an introduction to databases for data mining, and a study of advanced algorithms.

• 10-701 Machine Learning
• 10-702 Statistical Machine Learning
• 10-705 Intermediate Statistics
• 15-826 Multimedia Databases and Data Mining, (when 15-826 is not offered, 15-721 Database Management Systems may be taken instead).
• 15-750 Algorithms or 15-853 Algorithms in the Real World

Possible electives

10-910 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.

The Data Analysis Project requirement: 10-910
During the second year a Ph.D. student is required to demonstrate data mining 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 giving a ML colloquium on the Data Analysis Project report. Passing this requirement will be the judgment of the ML faculty, under the advice of the faculty advisor for the project.

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.

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.

Financial Support

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.

Application Information