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 coordinator

Dr. Bo Wang

lmp.grad@utoronto.ca for administrative queries.

Timings and location

Thursdays, 10:30 am - 12:30 pm

Location: University College, room 179

Evaluation methods

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

Schedule

Date	Topic
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