EE104/CME107: Introduction to Machine Learning

Stanford University, Spring Quarter, 2025

The schedule below shows what was covered in each lecture.

Tu 4/1	course information, overview and examples
Th 4/3	predictors slides 1-17, and knn_demo.jl
Tu 4/8	predictors slides 18-36
Th 4/10	validation slides, features slides 1-7, and reading_data.jl
Tu 4/15	features slides 8-37, and empirical risk minimization slides 1-6
Th 4/17	empirical risk minimization slides 7-22
Tu 4/22	empirical risk minimization slides 23-30, house prices slides 1-18
Th 4/24	non-quadratic losses, constant predictors slides 1-9
Tu 4/29	constant predictors slides 10-29, non-quadratic regularizers slides 1-9
Th 5/1	non-quadratic regularizers slides 10-23, neural networks, started neural demo
Tu 5/6	nn_demo.jl and classifiers slides 1-14
Th 5/8	classifiers slides 15-21 and ERM for classifiers and Boolean classification
Tu 5/13	multi-class classification, probabilistic classification slides 1-13
Th 5/15	probabilistic classification slides 14-25, ERM for probabilistic classification slides 1-13
Tu 5/20	ERM for probabilistic classification slides 16-21, unsupervised learning slides 1-24
Th 5/22	unsupervised learning slides 25-28, principal component analysis