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    An Introduction to Statistical Learning: With Applications in R (Springer Texts in Statistics) (Hardback) By (author) Gareth James, By (author) Trevor Hastie, By (author) Robert Tibshirani, By (author) Daniela Witten

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    DescriptionAn 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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  • Full bibliographic data for An Introduction to Statistical Learning

    Title
    An Introduction to Statistical Learning
    Subtitle
    With Applications in R
    Authors and contributors
    By (author) Gareth James, By (author) Trevor Hastie, By (author) Robert Tibshirani, By (author) Daniela Witten
    Physical properties
    Format: Hardback
    Number of pages: 440
    Width: 157 mm
    Height: 239 mm
    Thickness: 25 mm
    Weight: 885 g
    Language
    English
    ISBN
    ISBN 13: 9781461471370
    ISBN 10: 1461471370
    Classifications

    BIC E4L: MAT
    Nielsen BookScan Product Class 3: S7.8
    B&T Book Type: NF
    B&T Merchandise Category: TXT
    B&T Modifier: Region of Publication: 01
    LC subject heading: ,
    BIC subject category V2: PBT
    DC22: 519.5
    LC subject heading:
    B&T General Subject: 710
    B&T Modifier: Academic Level: 02
    LC classification: QA
    Ingram Subject Code: MA
    B&T Modifier: Text Format: 06, 01
    Warengruppen-Systematik des deutschen Buchhandels: 16280
    BISAC V2.8: MAT029000
    Abridged Dewey: 519
    BISAC V2.8: COM004000, COM077000, SCI040000
    Libri: STAT4000, MATH3690
    DC23: 519.5
    LC classification: TJ210.2-211.495, Q334-342, QA276-280
    Thema V1.0: PBT, UYQ, PHU, UFM
    Edition statement
    2013. Corr. 4th Printing 2014 ed.
    Illustrations note
    150 black & white illustrations, 10 black & white tables, biography
    Publisher
    Springer-Verlag New York Inc.
    Imprint name
    Springer-Verlag New York Inc.
    Publication date
    11 July 2014
    Publication City/Country
    New York, NY
    Author Information
    Gareth James is a professor of statistics at 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 assistant professor of biostatistics at University of Washington. Her research focuses largely on high-dimensional statistical machine learning. She has contributed to the translation of statistical learning techniques to the field of genomics, through collaborations and as a member of the Institute of Medicine committee that led to the report Evolution of Translational Omics. 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.
    Review quote
    From the book reviews: "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) "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)
    Back cover copy
    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.
    Table of contents
    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.