6.S897 | Spring 2019 | Graduate

Machine Learning for Healthcare


Class Meeting Times

Lecture: 2 sessions / week; 1.5 hours / session


This course requires at least an undergraduate level of machine learning which can be satisfied by 6.036 Introduction to Machine Learning or 6.862 Applied Machine Learning or 6.867 Machine Learning or 9.520J/6.860J Statistical Learning Theory and Applications or 6.806/6.864 Advanced Natural Language Processing or 6.438 Algorithms for Inference or 6.034 Artificial Intelligence.

Course Description

This course 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.


Assignments Percentages
Homework 40%
Course Projects 40%
Participation 20%

Participation includes lecture scribing, MLHC community consulting, and reading responses.

Lecture scribes

Each student is expected to either “scribe” for one lecture or “consult” for one Machine Learning Health Care (MLHC) community evening session (see below). A given lecture will have 1–2 scribes who are responsible for summarizing what was discussed in class. The first draft of the notes should be submitted to the Teaching Assistants by 11:59pm of the day after class (i.e. 30 hours after lecture ends). We will send you suggestions to revise, and once the notes are finalized, we will then post it on the course website. The goal will be to get the notes out by one week after the corresponding class.

We expect writing up lecture notes to take no more than 3 hours. If there are two scribes for one lecture, the two scribes should collaborate and submit one writeup. The notes you write should cover all the material covered during the relevant lecture, plus real references to the papers containing the covered material. Your notes should be understandable to someone who has not been to the lecture. You should write in full sentences where appropriate; point form is often too terse to follow without a sound track (though occasionally it is appropriate). Use numbered sections, subsections, etc. to organize the material hierarchically and with meaningful titles. Try to preserve the motivation, difficulties, solution ideas, failed attempts, and partial results obtained along the way in the actual lecture. 

MLHC Community Consulting

Each student is expected to either “scribe” for one lecture (see above) or “consult” for one Machine Learning for Healthcare (MLHC) community evening session. Throughout the semester, we will organize four evening sessions to engage with the larger MLHC community. Clinicians and other Boston area people interested in machine learning for healthcare will come to talk through their problems and ideas.

Students who sign up for community consulting will be expected to attend the entire session and submit a write-up of their experiences shortly after the session. We expect one write-up per clinician, so students should coordinate if they talked to the same clinician. Write-ups are due one week after the consulting session.

Problem Set Late Policy

  • 2 “slack” days: We understand that sometimes things outside one’s control prevent submitting by the deadline. As such, each student is given 2 “slack” days that they can use throughout the semester (e.g. you could submit two psets one day late each or you could submit one pset two days late) without a late penalty. the days do not subdivide into sub-day units: 2 hours late would spend one of the slack days without 22 hours of “rollover.” In your pdf writeup, specify how many slack days you are using (they cannot be used retroactively).
  • 10% off per unexcused late day: If you submit a pset 3 days late and use 1 slack day, then this is 2 unexcused late days, which translates to 20% off your homework.

In order to use a slack day, students must include it in writing on their submission pdf. Otherwise, TAs will assume no slack days used.


  • Sam uses 2 slack days on HW3. This is the first time Sam has used any slack days. Sam now has 0 remaining slack days and receives her homework score with no penalty.
  • Jamie uses 1 slack day on HW3 but submits 52 hours after the deadline. Therefore Jamie is 3 days late (rounded up) and receives 20% off the graded homework. This is the first time Jamie has used any slack days, so Jamie now has 1 slack day remaining.
Learning Resource Types
Lecture Videos
Lecture Notes