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

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

Course grade will be made up of:

**10%**Weekly Reflections**20%**Problem Sets**10%**Paper Presentation**60%**Course project

- Each week, students will
**select one paper**from the list above, and complete a series of reflection questions. - There will be ten reflections, each worth 1% of the final grade.
- Note that the reading (and associated reflection) are intended to be completed
**prior to**the lecture for that week. - Each weekly reflection will be due at
**12 pm on Thursday**(i.e. 1 hour prior to the start of lecture) on Markus. -
**Instructions and Questions**

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:

- What motivated the work
- What problem the paper is trying to solve
- The approach used in the paper
- The technical or clinical significance of the paper
- The secret terrible thing that a casual reader might not notice

Sign up for a presentation date and paper here.

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

- problem set 1 (last updated: Jan 16)
- Due:
**Feb 3 at 11:59 pm**on Markus - Worth 6.7% of the final grade
- Please complete the steps required to get access to MIMIC data outlined here. One of the steps will require you to fill out a Data Use Agreement (DUA) where you will be asked for:
- A reference name. Write Marzyeh Ghassemi.
- The general research area for which the data will be used: Write CS 2541 Homework

- problem set 2
- Due:
**Mar 1 at 11:59 pm**on Markus - Worth 13.3% of the final grade

- Project proposals (one per group):
**Feb 6 at 5pm** - Project presentations:
**Mar 26**and**Apr 2**in class - Project report (one per group): TBD

- 15% of the project grade
- Due
**Feb 6 at 5pm**on Markus. - Should be at most 3 pages, one per group
- Clearly state the following:
- Problem you wish to tackle
- Description of data you plan to use
- Proposed approach and methods
- Evaluation plan
- Timeline
- What each student in the group will do

- 15% of the project grade
- 15 minutes in class on either March 26 or April 2.
- Be prepared for questions after.
- Sign up TBD

- 70% of the project grade
- Due TBD on Markus
- The requirements for the report are:
- 8 pages (not including references)
- TBD conference format
- GitHub repository