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.