EE104/CME107: Introduction to Machine Learning

Stanford University, Spring Quarter, 2024

The schedule below is tentative and will be updated (frequently) as we progress through the quarter.

In the table below, VMLS refers to the ENGR104 textbook, Introduction to Applied Linear Algebra - Vectors, Matrices, and Least Squares.

Date Slides Additional reading
4/2 course information, overview and examples VMLS, chapters 12 and section 13.1.
4/4 predictors VMLS, section 13.1.
4/9 predictors, validation VMLS, section 13.2.
4/11 validation, features VMLS, section 13.3.
4/16 features VMLS, section 13.3.
4/18 empirical risk minimization and house prices example VMLS, section 15.4.
4/23 constant predictors, non-quadratic losses
4/25 non-quadratic regularizers
4/30 neural networks
5/2 classifiers
5/7 ERM for classifiers
5/9 Boolean classification, multi-class classification, probabilistic classification
5/14 ERM for probabilistic classification
5/16 unsupervised learning
5/21 principal component analysis
5/23 optimization
5/28 prox gradient method
5/30
6/4