Stanford CS229 — Machine Learning
Spring 2026

A broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning (generative learning, parametric/non-parametric learning, neural networks), unsupervised learning (clustering, dimensionality reduction), learning theory (bias/variance tradeoffs, practical advice), and reinforcement learning and adaptive control.

Taught by Chris Ré and Tengyu Ma, Stanford Department of Computer Science. Videos published by Stanford Online. Full syllabus and course materials (Stanford login required): cs229.stanford.edu