CSC2541HS: Topics in Machine Learning: Machine Learning for Health

Overview | Course Description | Grading | Schedule | Weekly Reflections | Paper Presentation | Problem Sets | Final Project

Overview

Instructor: Dr. Marzyeh Ghassemi
Time: Thursdays 1 PM - 3 PM
Room: GB 221
TAs: Taylor Killian, Nathan Ng, Haoran Zhang
Markus: https://markus.teach.cs.toronto.edu/csc2541-2020-01
Piazza: https://piazza.com/utoronto.ca/winter2020/csc2541


Course description

This course will give a broad overview of machine learning for health. We begin with an overview of what makes healthcare unique, and then explore machine learning methods for clinical and healthcare applications through recent papers. We discuss the recent successes of of graphical models, deep learning, time-series analysis, and transfer learning in the context of health. We also broadly cover concepts of learning, algorithmic fairness, interpretability, and causality. We emphasize the importance of collaboration between technical and non-technical researchers, and consider the implications of machine learning in healthcare governance and policy. Students will choose and complete a course project, and make project presentations at the end of the course.

This course requires a strong background in linear algebra and probability theory, or strong grades in the machine learning course. Familiarity with Python programming and software engineering is required.

CSC2541 will be capped to students who have an appropriate background this semester. If you are interested in taking the course, please come to our first lecture and fill out the course application https://forms.gle/6VL4mhGM4quSN8qd7


Grading

Course grade will be made up of:


Schedule

Week Date Lecture Materials Presentations Assignments
1 Jan 9, 2020
Why is healthcare unique?
2 Jan 16, 2020
Supervised Learning for Classification, Risk Scores and Survival
Week 2 Reflections
3 Jan 23, 2020
Clinical Time Series Modelling
Week 3 Reflections
4 Jan 30, 2020
Causal inference with Health Data --- Guest Lecture by Dr. Shalmali Joshi (Vector Institute)
Week 4 Reflections

Problem Set 1 (Feb 3 at 11:59pm)
5 Feb 6, 2020
Fairness, Ethics, and Healthcare
Week 5 Reflections

Project proposals (Feb 6 at 5pm)
6 Feb 13, 2020
Deep Learning in Medical Imaging -- Guest Lecture by Dr. Joseph Paul Cohen (MILA)
Week 6 Reflections
7 Feb 20, 2020
Clinical Reinforcement Learning -- Guest Lecture by Taylor Killian (UofT)
Week 7 Reflections
8 Feb 27, 2020
Clinical NLP and Audio -- Guest Lecture by Dr. Tristan Naumann (Microsoft Research)
Week 8 Reflections

Problem Set 2 (Mar 1 at 11:59pm)
9 Mar 5, 2020
Interpretability / Humans-In-The-Loop --- Guest Lecture by Dr. Rajesh Ranganath (NYU)
Week 9 Reflections
10 Mar 12, 2020
Disease Progression Modelling and Generalization/Transfer Learning -- Guest Lecture by Irene Chen (MIT)
Week 10 Reflections
11 Mar 19, 2020
Clinical Workflows
  • [Slides] Lecture 11
Week 11 Reflections
12 Mar 26, 2020
Course Presentations
13 Apr 2, 2020
Course Presentations
Project Report (Apr 3 at 11:59pm)


Weekly Reflections


Paper Presentations

The in-class paper presentations are worth 10% of your class grade. Presentations can be done on your own, or in teams of 2, and should be 15 minutes. Plan to cover:

  1. What motivated the work
  2. What problem the paper is trying to solve
  3. The approach used in the paper
  4. The technical or clinical significance of the paper
  5. The secret terrible thing that a casual reader might not notice
Ensure that you have 1-2 reflection questions to spark discussion at the end of the presentation.
Sign up for a presentation date and paper here.


Problem Sets

There will be two problem sets. Problem sets must be done individually.

Problem Set 1 - Clinical Time Series

Problem Set 2 - Fairness in Healthcare


Final Project


Project Deadlines

Groups

Collaboration: We strongly prefer 3-4 registered student per group, but we do permit groups with fewer students. Doing something related to your research is fine, but your class project should be distinct and you should be able to isolate your contributions to the project from those of any collaborators outside of the class.

Proposal

Presentation

Report