Free books on statistical learning

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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