Joint PhD Program in Machine Learning & Public Policy
Students in this program will be involved in courses and research from both the Machine Learning Department and the Heinz College. Students are expected both to make fundamental contributions to the science of machine learning as well as addressing core problems in one or more policy domains. During the first year the student will spend most of their time on coursework (75%) and research (25%). During subsequent years the research increases.
A sample curriculum is as follows:
FALL - 1st Year
SPRING - 1st Year
|10-715 Adv. Intro to Machine Learning||ML Core course|
|36-705 Intermediate Statistics||10-716 Advanced Machine Learning: Theory & Methods|
|90-908 Microeconomics||Social Science Course|
|90-901 Heinz PhD Seminar I||90-902 Heinz PhD Seminar II|
FALL - Year Two
SPRING - Year Two
|Heinz Advanced Elective||ML Core course|
|ML/Stat Advanced Elective||ML/PP Advanced Elective|
|90-918 Heinz PhD Seminar III|
Students must complete their first and second Heinz Research Papers by the end of year 2 and year 3 respectively.
Years 3 & 4
Thesis research co-supervised by a faculty in ML and a faculty in the Heinz College.