An Introduction to Statistical Learning : with applications in R / Gareth James

By: James, GarethContributor(s): James, Gareth | Witten, Daniela | Hastie, Trevor | Tibshirani, RobertMaterial type: TextTextPublisher number: :Technical Bureau India Pvt. Ltd. | :E/261, Shastri Nagar DelhiSeries: Springer texts in statisticsPublication details: New York : Springer, ©2021Description: xv, 607p 24cmISBN: 9781071614174Subject(s): Mathematics | Probabilities and applied mathematics | statistics | Mathematical models | R (Computer program language) | Linear RegressionDDC classification: 519.5 JAM
Contents:
Preface -- 1 Introduction -- 2 Statistical Learning -- 3 Linear Regression -- 4 Classification -- 5 Resampling Methods -- 6 Linear Model Selection and Regularization -- 7 Moving Beyond Linearity -- 8 Tree-Based Methods -- 9 Support Vector Machines -- 10 Deep Learning -- 11 Survival Analysis and Censored Data -- 12 Unsupervised Learning -- 13 Multiple Testing -- Index.
Summary: 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, deep learning, survival analysis, multiple testing, 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. This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility
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Holdings
Item type Current library Call number Status Notes Date due Barcode Item holds
Books Books SNU LIBRARY
519.5 JAM (Browse shelf(Opens below)) Not For Loan Books Shifted in Mathematics Dept. 28536
Total holds: 0

Preface --
1 Introduction --
2 Statistical Learning --
3 Linear Regression --
4 Classification --
5 Resampling Methods --
6 Linear Model Selection and Regularization --
7 Moving Beyond Linearity --
8 Tree-Based Methods --
9 Support Vector Machines --
10 Deep Learning --
11 Survival Analysis and Censored Data --
12 Unsupervised Learning --
13 Multiple Testing --
Index.


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, deep learning, survival analysis, multiple testing, 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. This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility

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