6.S897/HST.956: Machine Learning for Healthcare

Instructors: David Sontag, Peter Szolovits
Teaching Assistants: Willie Boag, Irene Chen
Graduate level; Units 3-0-9 (counts as an AAGS subject)
Time: Tuesdays & Thursdays, 2:30-4pm
Location: 4-270
Prerequisite: 6.036/6.862 or 6.867 or 9.520/6.860 or 6.806/6.864 or 6.438 or 6.034
Contact: Piazza

Course description

Introduces students to machine learning in healthcare, including the nature of clinical data and the use of machine learning for risk stratification, disease progression modeling, precision medicine, diagnosis, subtype discovery, and improving clinical workflows. Topics include causality, interpretability, algorithmic fairness, time-series analysis, graphical models, deep learning and transfer learning. Guest lectures by clinicians from the Boston area and course projects with real clinical data emphasize subtleties of working with clinical data and translating machine learning into clinical practice.


We are finalizing the schedule. Please see the 2017 course schedule as a beta version although we expect the course to change substantially. Note that the 2017 course was 6 credits, and this course is 12 credits.

Prerequisite quiz

This quiz will not count toward your grade, but will be used by the course staff to check prerequisites (6.036/6.862 or 6.867 or 9.520/6.860 or 6.806/6.864 or 6.438 or 6.034) and to assess students' preparation for this class.

Please complete the prerequisite quiz here. Submit it as soon as possible, and no later than Tuesday 2/5/19 at 11:59pm EST.

Problem sets

We expect there will be at least four problem sets this year.