An Introduction to Statistical Learning

Gareth James is a data sciences and operations professor at the University of Southern California. He has a large body of methodological work in the domain of statistical learning, with a focus on high-dimensional and functional data. His MBA elective courses in this area inspired the conceptual framework for this book.

Daniela Witten is a statistics and biostatistics associate professor at the University of Washington. Her research is primarily concerned with statistical machine learning in the high-dimensional setting, with a particular emphasis on unsupervised learning.

Trevor Hastie and Robert Tibshirani are Stanford University statistics professors and co-authors of the popular textbook Elements of Statistical Learning. Hastie and Tibshirani created generalized additive models and published a popular book with the same title.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, which is 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 over the last two decades. This book discusses some of the most important modeling and prediction techniques, as well as their applications. Linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and other topics are covered. The methods presented are illustrated with color graphics and real-world examples. Because the goal of this textbook is to make it easier for practitioners in science, industry, and other fields to use these statistical learning techniques, each chapter includes a tutorial on how to implement the analyses and methods presented in R, an extremely popular open source statistical software platform.

The Elements of Statistical Learning (Hastie, Tibshirani, and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers, was co-written by two of the authors. An Introduction to Statistical Learning covers many of the same topics, but at a much more accessible level. The book is also among the best books on statistics.

This book is intended for both statisticians and non-statisticians who want to analyze their data using cutting-edge statistical learning techniques. The text assumes only a basic understanding of linear regression and no prior knowledge of matrix algebra.

Author: Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

Link to buy: https://www.amazon.com/dp/1461471370

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