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

lmp.grad@utoronto.ca for administrative queries.

Timings and location

Thursdays, 10:30 am - 12:30 pm

Location: Sydney Smith building, Room 1072

Evaluation methods

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




January 11, 2024

Intro to ML in medicine, nearest neighbor classifier

January 18, 2024

Linear methods for regression and classification; tree-based classifier

Math diagnostic due

January 25, 2024

Introduction to Python; basic linear algebra; evaluation methods

February 1, 2024

ENSEMBLE-based methods; neural networks

1st assignment due

February 8, 2024

Supervised learning; Python tutorial for supervised learning practice

February 15, 2024

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

2nd assignment due

February 22, 2024

Reading week (no class)

February 29, 2024

Unsupervised learning for clustering: auto-encoder, graph-based methods; Python tutorial for unsupervised learning practice

March 7, 2024

Guest Lecturer: TBD

3rd assignment due

March 14, 2024

Guest Lecturer: TBD

March 21, 2024

Advanced deep learning methods for medical image analysis

March 28, 2024

Term project in-class presentation

April 4, 2024

Term project in-class presentation