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HLTH 658MMachine Learning Techniques in Health

Units
0.25
Level
Graduate
Faculty
Health
Subject
Public Health Sciences

HLTH 658M as listed in the Spring 2026 Graduate Calendar. The main page has the current version, when it runs, and seats.

This course focuses on the techniques of machine learning (ML) commonly used to solve healthcare and public health problems. Various analytics techniques, including data wrangling, visualization, unsurprised and supervised learning, will be introduced to students. Challenges and strategies related to missing data, imbalanced data and model selections will also be discussed. The learning activities consist of lectures, labs, and a final project to demonstrate the proficiency of ML techniques to solve population health or public health problems. Students who do not have the prerequisites but have completed an equivalent course that provides programming skills may be permitted to enrol in the course at the instructor’s discretion.

Prerequisites

  • Must have completed at least 1 of the following:
    • HLTH605A - Regression Models (0.50)
    • HLTH605B - Quantitative Methods and Analysis (0.50)
    • HLTH619 - Fundamental Research Methods in Health Informatics (0.50)

Antirequisites

None listed.

Leads to

Check it elsewhere for Spring 2026

Calendar versions

Graduate Calendar