An Introduction to Statistical Learning

by Gareth James, Daniela Witten Trevor Hastie, and Robert Tibshirani

This book is a very nice introduction to statistical learning theory. One of the great aspects of the book is that it is very practical in its approach, focusing much effort into making sure that the reader understands how to actually apply the techniques presented. The book does this by demonstrating their use in the freely available R language. At the end of each chapter are sample R sessions that present the inputs and outputs when running the various techniques discussed in the chapter text on actual data.

This is a great approach because it enables the reader to quickly study and experiment with a great number of machine learning using actual R code and data. It is then an easy exercise to modify the R code to work on different data sets if desired. For the applied statistician this is a great help because it cuts the research time down considerably. I'm not the only one who has a very high view of this book. Readers can see what others have said here.

To make sure I understood this material as well as possible, as I read the book, I worked all the conceptual and applied exercises at the end of each chapter. Linked to this page are the R scripts I wrote for each chapter. It is my hope that students of machine learning and statistics will find this material helpful. In addition to the R scripts I wrote up solutions to these exercises and put them in book form.

Originally these notes and solutions were written in PDF (using the mathematical typesetting language LaTeX). I converted the PDF format to a format I thought more people would find easier to read. You can preview and buy a kindle version of the book here. If you are interested in purchasing the PDF version you can do so for $41.00 (US dollars) via PayPal (see the link below).

Code for the Applied Exercises:
As always, I am interested in hearing about any errors that might exist in this material.

John Weatherwax