Instructor: Noémi Kreif, Assistant Professor, The CHOICE Institute (nkreif@uw.edu)
Location: Magnuson Health Sciences Center, Room T531
Meeting Times: Tuesdays & Thursdays, 11:30 a.m. – 12:50 p.m.
Credits: 3
About the Course: This course introduces machine learning methods, focusing on understanding the applied literature, gaining hands-on experience with basic analysis to address health outcomes, policy, and economics-related questions, and communicating results to a general scientific audience. Provides an overview of topics related to using machine learning for health policy analysis and medical decision-making, including model evaluation, algorithmic bias, model shift, and simulation modeling.
Prerequisite for HEOR 522: BIOST 511; BIOST 512; and BIOST 513 (which may be taken concurrently); or instructor permission
Recommended preparation: Graduate-level coursework in statistics, biostatistics, econometrics, or equivalent. Familiarity with multivariable statistical methods, including linear and logistic regression, and R statistical programming language.
Why take this course?
Level up your analytical toolkit and learn to apply machine learning (ML) to real-world challenges in:
- Health outcomes research
- Health policy evaluation
- Health economics
- Clinical decision-making
Across 10 weeks, students explore ML tools and their use in health:
- Course Overview & Intro to ML Concepts
- Supervised Learning — Regression
- Supervised Learning — Classification
- Advanced ML: Unsupervised Learning, Reinforcement Learning, LLMs
- ML for Causal Inference: Average Treatment Effects
- ML for Causal Inference: Heterogeneous Treatment Effects
- Clinical Algorithmic Discrimination
- ML for Simulation Modeling in Health Economics
- Explainable ML & Model Deployment
- Regulation of ML & AI in Health
Course Format
- In-person lectures
- Weekly homework assignments & presentations
- Real examples from published research
- Optional R demonstrations during class
- Recorded lectures available via Canvas
Undergraduate Section – PHARM 499B
We are excited to offer this course with an undergraduate parallel section as we continue to expand the School of Pharmacy’s undergraduate education programs! If you know undergraduate students who may be interested in enhancing their methodological and machine learning toolkits, they can enroll for Spring 2026 (with instructor permission) through PHARM 499 B. Check out this condensed syllabus for an overview of the undergraduate section and its learning outcomes and assessment methods.
- PHARM 499 B Machine Learning in Health Economics and Outcomes Research (3) Essential machine-learning methods for health research, with hands-on practice and a focus on interpreting results. Cross-listed with HEOR 522. Contact the instructor for permission. TTh 11:30 – 12:50 p.m.
Feel free to reach out with any questions: nkreif@uw.edu