(This article was originally published at Hyndsight, and syndicated at StatsBlogs.)
Hastie, Tibshirani and Friedman’s Elements of Statistical Learning first appeared in 2001 and is already a classic. It is my go-to book when I need a quick refresher on a machine learning algorithm. I like it because it is written using the language and perspective of statistics, and provides a very useful entry point into the literature of machine learning which has its own terminology for statistical concepts. A free downloadable pdf version is available on the website.
Recently, a simpler related book appeared entitled Introduction to Statistical Learning with applications in R by James, Witten, Hastie and Tibshirani. It “is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences”. This would be a great textbook for our new 3rd year subject on Business Analytics. The R code is a welcome addition in showing how to implement the methods. Again, a free downloadable pdf version is available on the website.
There is also a new, free book on Statistical foundations of machine learning by Bontempi and Ben Taieb available on the OTexts platform. This is more of a handbook and is written by two authors coming from a machine learning background. R code is also provided. Being an OTexts book, it is continually updated and revised, and is freely available to anyone with a browser.
Thanks to the authors for being willing to make these books freely available.
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