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. 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%)
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 |