This Ph.D. program differs from the Machine Learning Ph.D. program in that it places significantly more emphasis on preparation in statistical theory and methodology. Similarly, this program differs from the Statistics Ph.D. program in its emphasis on machine learning and computer science. (See below for 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 adviser is in the Department of Statistics. In years two and beyond, thesis research is co-supervised by a faculty in machine learning and a faculty in statistics, or supervised by a faculty member with a joint appointment. The thesis committee must contain at least one member with a home department of statistics and one with home department of machine learning.