Elements of Statistical Learning

Summary of notes and learnings from The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman.

Chapter 2 Overview of Supervised Learning

• 2.3 Least squares and Nearest Neighbors - Derivations | Exercises
• 2.5 Local Methods in High Dimensions - Derivations

Chapter 3 Linear Methods for Regression

• 3.2 Linear Regression Models and Least Squares - Derivations | Exercises | Summary
• 3.3 Subset Selection - Derivations | Exercises | Summary
• 3.4 Shrinkage Methods - Derivations | Exercises | Summary
• 3.5 Methods using Derived Input Directions - Exercises | Summary
• 3.7 Multiple Outcome Shrinkage and Selection - Exercises

Chapter 4 Linear Methods for Classification

• 4.1 Introduction - Summary
• 4.2 Linear Regression of an Indicator Matrix - Summary
• 4.3 Linear Discriminant Analysis - Derivations | Exercises | Summary
• 4.4 Logistic Regression - Derivations | Exercises | Implementation | Summary
• 4.5 Separating Hyperplanes - Derivations | Exercises | Implementation | Summary

Chapter 7 Model Assessment and Selection

• 7.2 Bias, Variance, and Model Complexity - Summary
• 7.3 The Bias-Variance Decomposition - Derivations | Exercises | Summary
• 7.4 Optimism of the Training Error Rate - Exercises | Summary
• 7.5 Estimates of In-Sample Prediction Error - Summary
• 7.6 Effective Number of Parameters - Exercises | Summary