An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) (英語) ハードカバー – 2013/8/12
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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.
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)商品の説明をすべて表示する
ISL is neither as comprehensive nor as in-depth as ESL. It is, however, an excellent introduction to Learning due to the ability of the authors to strike a perfect balance between theory and practice. Theory is there to aim the reader as to understand the purpose and the "R Labs" at the end of each chapter are as valuable (or perhaps even more) than the end-of-chapter exercises.
ISL is an excellent choice for a two-semester advanced undergraduate (or early graduate) course, practitioners trained in classical statistics who want to enter the Learning space, and seasoned Machine Learners. It is especially helpful for getting the fundamentals down without being bogged down in heavy mathematical theory, a great way to kick-off corporate Learning units, or as an aid to help statisticians and learners communicate better.
A needed and welcome addition to the Learning literature, authored by some of the most well respected names in industry and academia. A classic in the making. Recommended unreservedly.
UPDATE (12/17/2013): Two of the authors (Hastie & Tibshirani) are offering a 10-week free online course (StatLearning: Statistical Learning) based on this book found at Stanford University's Web site (Starting Jan. 21, 2014). They also say that "As of January 5, 2014, the pdf for this book will be available for free, with the consent of the publisher, on the book website." Amazing opportunity! Enjoy!
UPDATE (04/03/2014): I took the course above and found it very helpful and insightful. You don't need the course to understand the book. If anything, the course videos are less detailed than the book. It is certainly nice, though, to see the actual authors explain the material. Also, the interviews by Efron and Friedman were a nice touch. The course will be offered again in the future.
Well, I'm lucky (and probably so are you) because in 2013 Stanford Statistics professors James/Witten/Hastie/Tibshirani wrote this simpler 'An Introduction to Statistical Learning' that requires only a Bachelor's degree in Mathematics or Statistics. If you have that math grounding, then this is a wonderful book to start your Statistical Learning. The book offers a clear application of Mathematical Statistics and the programming language R to Statistical Learning. At the end of each chapter, the authors provide 10-15 questions to test whether you've digested the material.
Only a few times have I needed to review my Hogg/Craig 'Introduction to Mathematical Statistics'. If you want an excellent book on Mathematical Statistics to prepare you for both 'Introduction to Statistical Learning' and 'The Elements of Statistical Learning', buy the 7th edition of 'Introduction to Mathematical Statistics' by Hogg/McKean/Craig, which is typically used for a year-long (2 semesters) class for 1st or 2nd year graduate students in Mathematics or Statistics. In fact, you could simply bone up on Hogg/McKean/Craig, skip 'Introduction to Statistical Learning', and go straight to the more challenging 'Elements of Statistical Learning'. I wanted to digest some Statistical Learning asap and probably so will you. Enjoy.