LMP1210H: Basic Principles of Machine Learning in Biomedical Research

Who can attend

You must be registered in a graduate program to attend this course.

This course is open to all graduate students at the University of Toronto, provided you have pre-approval from your department and the course coordinators. 

Course description

This course is intended for graduate students in Health Sciences to learn the basic principles of machine learning in biomedical research and to build and strengthen their computational skills of medical research. The goal is to establish an essential foundation for graduate students to take the first steps in computational research in medicine.

The course aims to equip you with the fundamental knowledge of machine learning (ML). During the course, you will acquire basic computational skills and hands-on experience to deploy ML algorithms using python. You will learn the current practices and applications of ML in medicine, and understand what ML can and cannot do for medicine.

  • Introduction to basic principles and current practices of machine learning in biomedical research.
  • Focus on the fundamental ML algorithms with applications in biomedical data
  • The application of unsupervised learning in genomic data
  • The application of supervised learning for medical images.

Course coordinators

Dr. Rahul Krishnan

Dr. Anna Goldenberg

Dr. Sana Tonekaboni (Department of Computer Science) sana.tonekaboni@mail.utoronto.ca

lmp.grad@utoronto.ca for administrative queries.

Timings and location

Thursdays, 10:30 am - 12:30 pm

Location: Building: Sidney Smith Hall (SS), room 1072

Evaluation methods

  • Three assignments (45%)
  • Term project on machine learning algorithms in medicine (40%)
  • In-class participation (15%)




January 10, 2022

Intro to ML in medicine, nearest neighbor classifier

January 17, 2022

Introduction to python and evaluation methods

January 24, 2022

Linear methods for regression and classification; tree-based classifier

1st assignment due

January 31, 2022

ENSEMBLE-based methods; neural networks

February 7, 2022

Python tutorial for supervised learning practice

February 14, 2022

Unsupervised learning for clustering: K-means, Gaussian mixture models

2nd assignment due

February 21, 2022

Reading week (no class)

February 28, 2022

Unsupervised learning for clustering: auto-encoder, graph-based materials

Python tutorial for unsupervised learning

March 7, 2022

Case study I: single-cell analysis using unsupervised learning

March 14, 2022

Case study II: cell type classification using supervised learning

March 21, 2022

Advanced deep learning methods for medical image analysis

3rd assignment due

March 28, 2022

Term project in-class presentation

April 4, 2022

Term project in-class presentation