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The Elements of Statistical Learning Stanford University. 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, No, a free online version of An Introduction to Statistical Learning, with Applications in R by James, Witten, Hastie and Tibshirani (Springer, 2013) is available from that website. Springer has agreed to this, so no need to worry about copyright. Of course you may not distribiute printed versions of this pdf file..

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The go-to bible for this data scientist and many others is The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert The emphasis is on supervised learning, but the course addresses the elements of both supervised learning and unsupervised learning. It covers essential material for developing new statistical learning algorithms. Prerequisites Calculus-level probability and statistics, such as in CSI 672, and some general knowledge of applied statistics.

The go-to bible for this data scientist and many others is The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert The Elements of Statistical Learning: Published in Statistical Applications in Genetics and R "Learning from Data: Statistical Advances and Challenges

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No, a free online version of An Introduction to Statistical Learning, with Applications in R by James, Witten, Hastie and Tibshirani (Springer, 2013) is available from that website. Springer has agreed to this, so no need to worry about copyright. Of course you may not distribiute printed versions of this pdf file. An introduction to statistical learning : with applications in R / Gareth James, Two of the authors co-wrote The Elements of Statistical Learning

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Recently, a simpler related book appeared entitled Introduction to Statistical Learning with applications in R by James, Witten, Hastie and Tibshirani. It вЂњis aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciencesвЂќ. It covers much of the same material as Elements of Statistical Learning, but at a level more accessible to a broad audience and includes many examples of applied statistical learning using R, a domain-specific language for statistical computing. The course, like the book, will include many practical examples of statistical computing using R.

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An Introduction to Statistical Learning with Applications in R is written at a more introductory level with less mathematical background required than The Elements of Statistical Learning, makes use of R (unlike The Elements of Statistical Learning), and was first published in 2013, some years after this thread was started. 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.

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