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HLTH 658MMachine Learning Techniques in Health

Units
0.25
Level
Graduate
Faculty
Health
Subject
Public Health Sciences

When it runs

  1. F24
  2. W25
  3. S25
  4. F25
  5. W26
  6. S26
  7. F26
  8. W27
  9. S27

Ran in 1 of the last 7 terms. Winter 2027: scheduled, 2 lecture sections. Spring 2027: schedule not out yet.

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

Winter 2027 sections

Enrolment from UW Open Data, which can trail Quest by up to two days. Instructors and rooms: Schedule of Classes.
SectionClassEnrolledMeets
LEC 00130530 / 35Th 11:30–14:20
LEC 00230630 / 35Online or by arrangement

Check it elsewhere for Fall 2026

Calendar versions

Graduate Calendar