CSC2541HS: Topics in Machine Learning:
Machine Learning for Health
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 programming and software engineering is beneficial, but not required.
CS2541 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://goo.gl/forms/DFm2SPYZTUiVrsEk2
Overview
Instructor: Dr. Marzyeh Ghassemi
Course Title: Topics in Machine Learning: Machine Learning for Health
Time: Thursdays 10 AM - 12 PM
Room: MS 4279
TAs: Bret Nestor, Sayyed Nezhadi, Bai Li
Markus: https://markus.teach.cs.toronto.edu/csc2541-2019-01
Piazza: https://piazza.com/utoronto.ca/winter2019/csc2541
Grading
Course grade will be made up of:
- 15% Homework (1 problem set) *
- 10% Weekly reflections submitted in Markus for all required papers in reading list (1-2 questions)
- 15% Paper presentation done on a topic in-class (sign-up after the first lecture)
- 60% Course project (an eight-page write up of your application of ML approaches to a healthcare problem)
- A reference name. Write Marzyeh Ghassemi
- The general research area for which the data will be used: Write CS 2541 Homework