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Springer Texts in Statistics #103: An Introduction to Statistical Learning: With Applications in R

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Synopses & Reviews

Publisher Comments:

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.

Synopsis:

This book presents key modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering.

Synopsis:

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.

About the Author

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.

Table of Contents

Statistical learning. -Linear and logistic regression. - Subset selection and coefficient shrinkage. - Moving beyond linearity. - Neural netweorks. - Tree based regression and classification methods. - Bagging and bootsting methods. - Support vector machines. - Kernel methods. - Unsupervised learning. - Option pricing using statistical learning.

Product Details

ISBN:
9781461471370
Author:
James, Gareth
Publisher:
Springer
Author:
Hastie, Trevor
Author:
Witten, Daniela
Author:
Tibshirani, Robert
Location:
New York, NY
Subject:
Statistics
Subject:
Earth sciences, geography, environment, planning
Subject:
Data mining
Subject:
Inference
Subject:
R software
Subject:
Statistical Learning
Subject:
Supervised learning
Subject:
Unsupervised learning
Subject:
Statistical Theory and Methods
Subject:
Statistics and Computing/Statistics Programs
Subject:
Probability and Statistics in Computer Science
Subject:
Statistics/General
Subject:
Mathematics | Probability and Statistics
Subject:
Theoretical, Mathematical and Computational Physics
Subject:
The Arts
Subject:
mathematics and statistics
Subject:
Mathematical statistics
Copyright:
Edition Description:
2013
Series:
Springer Texts in Statistics
Series Volume:
103
Publication Date:
20130710
Binding:
HARDCOVER
Language:
English
Pages:
430
Dimensions:
235 x 155 mm

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Springer Texts in Statistics #103: An Introduction to Statistical Learning: With Applications in R New Hardcover
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$87.75 In Stock
Product details 430 pages Springer - English 9781461471370 Reviews:
"Synopsis" by , This book presents key modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering.
"Synopsis" by , 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.
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