More and more interesting research is being done at the boundary between Machine learning and Statistics. This is reflected at Carnegie Mellon by the strong ties between the Machine Learning Department and the Department of Statistics. 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.
This PhD program differs from the Machine Learning PhD program in that it places significantly more emphasis on preparation in statistical theory and methodology. Similarly, this program differs from the Statistics PhD program in its emphasis on machine learning and computer science. (See below for a more details on the course requirements.)
Students in this track will be involved in courses and research from both the Departments of Statistics and Machine Learning. During the first year, students will normally be situated in the Department of Statistics. During later years, students will normally be located in the Machine Learning Department unless the primary advisor is in the Department of Statistics.
The typical curriculum is as follows:
(36- designates a statistics course. 10- designates a Machine Learning course, 15- designates a CS course.)
A * indicates a course that is in the joint program but not in the Machine Learning
A # indicates a course that is in the joint program but not in the
Statistics PhD program.
|Fall 1||Spring 1|
|36-705 Intermediate Statistics||*36-757 Advanced Data Analysis (ADA) I|
|10-715 Advanced Introduction to Machine Learning||10-702 Statistical Machine Learning|
|10-915 Journal Club (or in Spring 1)||*36-752 Advanced Prob. Overview|
|Fall 2||Spring 2|
|*36-755 Advanced Statistics||#15-750 Algorithms or 15-853 Algorithms in the Real World|
|*36-758 Advanced Data Analysis II|
|Years 2 and after|
|Thesis research co-supervised by someone in Machine Learning and someone in Statistics.|