BIOST 2155
Introductory Statistical Learning for Health Sciences
A graduate-level introduction to statistical learning methods, with applications drawn from modern health science data. Topics include linear regression with regularization, classification, clustering, dimensionality reduction, and ensemble methods.
Course at a glance
| Term | Fall 2026 |
| Credits | 2 |
| Lecture | Fridays 9:00–10:50 AM, 2121C Public Health |
| Office hours | Fridays 2:00–3:00 PM, A740 Public Health (or by appointment) |
| Instructor | Soumik Purkayastha · soumik@pitt.edu |
Where to start
- The schedule lists each week with slides, readings, and code.
- The assignments page has the three homeworks.
- The project page describes the final project.
- The syllabus covers grading, policies, and prerequisites.
Recommended textbook
James, Witten, Hastie & Tibshirani. An Introduction to Statistical Learning: with Applications in R. Springer, 2013. Free PDF
Optional references
- Hastie–Tibshirani–Friedman Elements of Statistical Learning
- Bishop Pattern Recognition and Machine Learning
- Prince Understanding Deep Learning.