The Elements of Statistical Learning

by Jerome Friedman, Trevor Hastie, and Robert Tibshirani

Introduction
This is an excellent textbook on statistical learning theory and I would recommend it highly to anyone who is working in the areas of statistical learning, pattern recognition, or machine learning. More information about this book (and an electronic version) can be found here.

Note that the data and some routines from this book have been bundled into an R package that can be found here.

You can find notes on various sections of the book, solutions to selected problems, and numerical code for problems and some examples from the second edition at the links below.

• The Most Recent Solution Manual

• 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)
  
• Chapter 10 (Boosting and Additive Trees)
  
• Chapter 15 (Random Forests)