The Elements of Statistical Learning

This repository documents my progress as I work through The Elements of Statistical Learning by T. Hastie, R. Tibshirani, and J. Friedman. I include my solutions to the exercises in each chapter as well as my own additional derivations and R implementations of some of the sections that I have found particularly interesting and/or tricky along the way. Click on any of the links below to see a more detailed description of my workings for each chapter. If you notice any errors or have a question please feel free to raise an issue.

• Chapter 2 - Overview of Supervised Learning
• Chapter 3 - Linear Methods for Regression
• Chapter 4 - Linear Methods for Classification
• Chapter 5 - Basis Expansions and Regularization
• Chapter 6 - Kernel Smoothing Methods
• Chapter 7 - Model Assessment and Selection