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Applied Machine Learning in Medicine

Learn machine learning by doing it. Over 8 Friday evenings you'll go from a clinical question to a cohort, a model, and a final presentation, all on the NIH All of Us research dataset. Past student projects have been published in peer-reviewed journals.

When
Summer 2026 · Fridays, 6:00 to 7:30 PM
Where
Scaife Hall
Seats
40 students
Data
NIH All of Us Research Program
Status
Completed

The Summer 2026 cohort has wrapped up. Want a seat next time?

Tell me when it opens

Schedule

Each session: hands-on work 6:00 to 6:45, then machine learning theory 6:45 to 7:30.

  1. Week 1June 5

    Introduction to All of Us and project goals

    Theory: What is machine learning? Key concepts and terminology

    Ethan Wu

  2. Week 2June 12

    Project planning and an example project

    Theory: Regression: linear and logistic models

    Ethan Wu

  3. Week 3June 19

    All of Us cohort and dataset building

    Theory: Interpretable modeling: training, testing, and preventing overfitting

    Alexis Cenname, Ethan Wu

  4. Week 4June 26

    Python and pandas crash course

    Theory: Decision trees and model evaluation: AUC, SHAP

    Ethan Wu

  5. Week 5July 3

    Optional mini-hackathon meetup

    Async, optional

  6. Week 6July 10

    Feature selection, overfitting, generalization

    Theory: Neural networks

    Ethan Wu

  7. Week 7July 17

    Project work session

    Ethan Wu

  8. Week 8July 24

    Final presentations

    Ethan Wu

Final project

Work solo or in a team of two. Pick a clinical question, build case and control cohorts from All of Us, train and evaluate models, and present what you found.

Instructors

  • Ethan Wu · lead instructor
  • Dr. Shyam Visweswaran
  • Dr. Richard Steinman
  • Dr. Vanathi Gopalakrishnan
  • Dr. Ansuman Chattopadhyay
  • Alexis Cenname, MS

Resources

Useful whether or not you're enrolled.

Questions about the course? etw46@pitt.edu. Attendance is expected but not enforced.