gadamc/the-elements-of-statistical-learning

My notes and codes (jupyter notebooks) for the "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani and Jerome Friedman

Jupyter notebooks for the book "The Elements of Statistical Learning".

This repository contains Jupyter notebooks implementing the algorithms found in the book and summary of the textbook.

Table of Contents

• Chapter 2

  • 2.3 Least Squares and Nearest Neighbors
  • 2.4 Statistical Decision Theory
  • 2.5 Local Methods in High Dimensions
  • 2.6 Statistical Models, Supervised Learning and Function Approximation
  • 2.7 Structured Regression Models
  • 2.8 Classes of Restricted Estimators
  • 2.9 Model Selection and the Bias-Variance Tradeoff

• Chapter 3

  • 3.1 Introduction
  • 3.2 Linear Regression Models and Least Squares
  • 3.2.1 Example Prostate Cancer
  • 3.2.2 The Gauss–Markov Theorem
  • 3.2.3 Multiple Regression From Simple Univariate Regression
  • 3.2.4 Multiple Outputs
  • 3.3 Subset Selection
  • 3.4 Shrinkage Methods
  • 3.4.1 Ridge Regression
  • 3.4.2 The Lasso
  • TODO: 3.4.3 Discussion: Subset Selection, Ridge Regression and the Lasso
  • 3.4.4 Least Angle Regression
  • 3.5 Methods Using Derived Input Directions

• Chapter 4

  • 4.1 Introduction
  • 4.2 Linear Regression of an Indicator Matrix
  • 4.3 Linear Discriminant Analysis
  • 4.3.1 Regularized Discriminant Analysis
  • 4.3.2 Computations for LDA
  • 4.3.3 Reduced-Rank Linear Discriminant Analysis
  • 4.4 Logistic Regression
  • 4.4.1 Fitting Logistic Regression Models
  • 4.4.2 Example: South African Heart Disease
  • 4.4.3 Quadratic Approximations and Inference
  • 4.4.4 L1 Regularized Logistic Regression

• Chapter 11

  • WIP: 11.7 Example: ZIP Code Data