Carnegie Mellon University

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.