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.
This PhD program differs from the Machine Learning PhD program in that it places significantly more emphasis on preparation in statistical theory and methology. Similarly, this program differs from the Statistics PhD program in its emphasis on machine learning and computer science. The Joint Ph.D. Program in Machine Learning and Statistics is a new program aimed at preparing students for academic careers in both CS and Statistics departments at top universities.
The Joint Ph.D. Program in Machine Learning and Public Policy is a new program operated jointly by faculty in Machine Learning and the Heinz College (Schools of Public Policy, Information Systems, and Management). Students will gain the skills necessary to develop new state-of-the-art machine learning technologies and apply these successfully to real-world policy domains.
The Graduate Training Program of the Center for the Neural Basis of Cognition offers exciting interdisciplinary training opportunities in conjunction with many affiliated Ph.D. granting programs and departments. If you are a prospective graduate student, see the Letter from the Co-Directors for a brief statement of the overall goals of the program, or visit The Graduate Student Experience to learn about the program from the perspective of our current students. Detailed information about our program is also available under the Program Description, Class Schedule, and Ethics Education links.
If you are interested in joining us in the CNBC, please note that you must be admitted to one of the affiliated Ph.D. programs at either the University of Pittsburgh or Carnegie Mellon. Details on the application process as well as a form for your application to the CNBC can be found by visiting our Application Process page.