Introduction.- Statistical Learning.- Linear Regression.- Classification.- Resampling Methods.- Linear Model Selection and Regularization.- Moving Beyond Linearity.- Tree-Based Methods.- Support Vector Machines.- Unsupervised Learning.- Index.
"An Introduction to Statistical Learning (ISL)" by James, Witten, Hastie and Tibshirani is the "how to" manual for statistical learning. Inspired by "The Elements of Statistical Learning" (Hastie, Tibshirani and Friedman), this book provides clear and intuitive guidance on how to implement cutting edge statistical and machine learning methods. ISL makes modern methods accessible to a wide audience without requiring a background in Statistics or Computer Science. The authors give precise, practical explanations of what methods are available, and when to use them, including explicit R code. Anyone who wants to intelligently analyze complex data should own this book." (Larry Wasserman, Professor, Department of Statistics and Machine Learning Department, Carnegie Mellon University)
Gareth James is a professor of data sciences and operations at the
University of Southern California. He has published an extensive
body of methodological work in the domain of statistical learning
with particular emphasis on high-dimensional and functional data.
The conceptual framework for this book grew out of his MBA elective
courses in this area.Daniela Witten is an associate professor of
statistics and biostatistics at the University of Washington. Her
research focuses largely on statistical machine learning in the
high-dimensional setting, with an emphasis on unsupervised
Trevor Hastie and Robert Tibshirani are professors of statistics at Stanford University, and are co-authors of the successful textbook Elements of Statistical Learning. Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap.
Poullis, Computing Reviews, September, 2014)
"The book provides a good introduction to R. The code for all the statistical methods introduced in the book is carefully explained. ... the book will certainly be useful to many people (including me). I will surely use many examples, labs and datasets from this book in my own lectures." (Pierre Alquier, Mathematical Reviews, July, 2014)"The stated purpose of this book is to facilitate the transition of statistical learning to mainstream. ... it adds information by including more detail and R code to some of the topics in Elements of Statistical Learning. ... I am having a lot of fun playing with the code that goes with book. I am glad that this was written." (Mary Anne, Cats and Dogs with Data, maryannedata.com, June, 2014)"This book (ISL) is a great Master's level introduction to statistical learning: statistics for complex datasets. ... the homework problems in ISL are at a Master's level for students who want to learn how to use statistical learning methods to analyze data. ... ISL contains 12 very valuable R labs that show how to use many of the statistical learning methods with the R package ISLR ... ." (David Olive, Technometrics, Vol. 56 (2), May, 2014)"Written by four experts of the field, this book offers an excellent entry to statistical learning to a broad audience, including those without strong background in mathematics. ... The end-of-chapter exercises make the book an ideal text for both classroom learning and self-study. ... The book is suitable for anyone interested in using statistical learning tools to analyze data. It can be used as a textbook for advanced undergraduate and master's students in statistics or related quantitative fields." (Jianhua Z. Huang, Journal of Agricultural, Biological, and Environmental Statistics, Vol. 19, 2014)"It aims to introduce modern statistical learning methods to students, researchers and practitioners who are primarily interested in analysing data and want to be confined only with the implementation of the statistical methodology and subsequent interpretation of the results. ... the book also demonstrates how to apply these methods using various R packages by providing detailed worked examples using interesting real data applications." (Klaus Nordhausen, International Statistical Review, Vol. 82 (1), 2014)"The book is structured in ten chapters covering tools for modeling and mining of complex real life data sets. ... The style is suitable for undergraduates and researchers ... and the understanding of concepts is facilitated by the exercises, both practical and theoretical, which accompany every chapter." (Irina Ioana Mohorianu, zbMATH, Vol. 1281, 2014) "The book excels in providing the theoretical and mathematical basis for machine learning, and now at long last, a practical view with the inclusion of R programming examples. It is the latter portion of the update that I've been waiting for as it directly applies to my work in data science. Give the new state of this book, I'd classify it as the authoritative text for any machine learning practitioner...This is one book you need to get if you're serious about this growing field." (Daniel Gutierrez, Inside Big Data, inside-bigdata.com, October 2013)