An Introduction to Statistical Learning: with Applications in R (Springer Texts
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- Book Title
- An Introduction to Statistical Learning: with Applications in R (
- ISBN
- 9781461471370
Über dieses Produkt
Product Identifiers
Publisher
Springer New York
ISBN-10
1461471370
ISBN-13
9781461471370
eBay Product ID (ePID)
159944459
Product Key Features
Number of Pages
Xiv, 426 Pages
Publication Name
Introduction to Statistical Learning : with Applications in R
Language
English
Publication Year
2017
Subject
Mathematical & Statistical Software, Intelligence (Ai) & Semantics, Probability & Statistics / General
Type
Textbook
Subject Area
Mathematics, Computers
Series
Springer Texts in Statistics Ser.
Format
Hardcover
Dimensions
Item Height
0.9 in
Item Weight
35.8 Oz
Item Length
9.5 in
Item Width
6.4 in
Additional Product Features
Intended Audience
Scholarly & Professional
Dewey Edition
23
Reviews
From the reviews: "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) "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) "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) , "Data and statistics are an increasingly important part of modern life, and nearly everyone would be better off with a deeper understanding of the tools that help explain our world. Even if you don't want to become a data analyst--which happens to be one of the fastest-growing jobs out there, just so you know--these books are invaluable guides to help explain what's going on." (Pocket, February 23, 2018), From the reviews: "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) , "...Besides the obvious expertise of the authors in this field, another reason why the goal of the book is reached so successfully is the structure of each chapter. A detailed lab section follows at the end of each chapter which illustrates the application to example data sets in R accompanied by the annotated R code. The chapters close with conceptual and applied exercises. All data used in this book are either already in R or are provided in an R package accompanying the book and the code from the lab sessions is also available on the book's Web page...These two books ['An Introduction to Statistical Learning' and 'The Elements of Statistical Learning'] will go very well together, especially when teaching these methods to undergraduate students in statistics or computer science or to students from applied fields." International Statistical Review (2014), 82, 1, review by Klaus Nordhausen "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, From the book reviews: "This book has a very strong advantage that sets it well ahead of the competition when it comes to learning about machine learning: it covers all of the necessary details that one has to know in order to apply or implement a machine learning algorithm in a real-world problem. Hence, this book will definitely be of interest to readers from many fields, ranging from computer science to business administration and marketing." (Charalambos 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), 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), "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, 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), From the book reviews: "This book has a very strong advantage that sets it well ahead of the competition when it comes to learning about machine learning: it covers all of the necessary details that one has to know in order to apply or implement a machine learning algorithm in a real-world problem. Hence, this book will definitely be of interest to readers from many fields, ranging from computer science to business administration and marketing." (Charalambos 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) "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)
TitleLeading
An
Series Volume Number
103
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
519.5
Table Of Content
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.
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., 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., 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.
LC Classification Number
QA276-280
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