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An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani · 3 HN comments
HN Books has aggregated all Hacker News stories and comments that mention "An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)" by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.
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Amazon Summary
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
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Hacker News Stories and Comments

All the comments and stories posted to Hacker News that reference this book.
Pretty much any book by W. Richard Stevens, but in particular Unix Network Programming, which made a cameo appearance in Wayne's World.

Intro to Statistical Learning by Hastie, Tibshirani, James and Witten: https://www.amazon.com/Introduction-Statistical-Learning-App...

Thanks for the link.

https://www.amazon.com/Elements-Statistical-Learning-Predict...

vs

https://www.amazon.com/Introduction-Statistical-Learning-App...

The former seems to assume prior knowledge and the latter seems more beginner friendly, except it uses R (in case anyone wants the distinction).

⬐ disgruntledphd2
R is well worth learning if you're going to be doing any serious work with data. It's a weird-ass language, but it does data analysis/graphing/modelling really, really well.
⬐ gubbe
These books assume some degree of Linear Algebra, ESL is definitely not for a entry level.

I would like to add www.codingthe matrix.com for any developers, which has a MOOC as well (Coursera), as a complement to Strang.

The best introductory book and also the most cohesive one is "Learning from Data" from Yaser Abu-Mostafa, accompanied by great video lectures:

http://amlbook.com/

It differs from other books in that all the material is treated from the unified perspective of statistical learning theory and VC dimension, as a result the book feels less like a hodgepodge of unrelated techniques and more like an introduction to a coherent field.

Hastie and Tibshirani also have a new, less demanding mathematically book out:

http://www.amazon.com/Introduction-Statistical-Learning-Appl...

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