We do not have a formal textbook for this class, but I am still providing optional readings. The content of this course is heavily influenced by these books, both both freely available:

ESL - The Elements of Statistical Learning

ISL - An Introduction to Statistical Learning

At a minimum, skim through the text and look at the visualizations. Here's two examples of the great stuff you're missing out on if you don't.

The readings are as follows:

• Broad concepts, KNN regression, linear regression: ESL 1,2.1-2.6
• Shrinkage / Regularization + Model / Feature Selection: ESL 3.1,3.2 up to Eq. 3.7 (skip p. 47-56), 3.3, 3.4,3.8.6 and 3.9
• Train / Test / Validation: ISL 5.1-5.1.4
• Regression Trees / Classification: ESL 9.2.1-9.2.3
• Neural Networks: notes (we distribute)
• Kernel Methods: ESL 5.1, 5.2(skim), 5.8,12.1-12.3
• Bayesian Inference: notes (we distribute)
• K-means: ESL 14.3.5-14.3.6
• Dimensionality Reduction: ESL 14.5

Notation guide:

There are numerous differences in notation, but here are the major ones: