Verwandte Artikel zu An Introduction to Statistical Learning: with Applications...
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) - Softcover
Inhaltsangabe
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 naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
Über die Autorin bzw. den Autor
Gareth James is a professor of data sciences and operations, and the E. Morgan Stanley Chair in Business Administration, at the 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 a professor of statistics and biostatistics, and the Dorothy Gilford Endowed Chair, at the University of Washington. Her research focuses largely on statistical machine learning techniques for the analysis of complex, messy, and large-scale data, with an emphasis on unsupervised learning.
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.
Von der hinteren Coverseite
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 naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
Neu kaufen
Diesen Artikel anzeigen
EUR 69,16
Gratis für den Versand innerhalb von/der Deutschland
Versandziele, Kosten & Dauer
Suchergebnisse für An Introduction to Statistical Learning: with Applications...
Beispielbild für diese ISBN
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
Anbieter: Books From California, Simi Valley, CA, USA
Verkäuferbewertung 5 von 5 Sternen
paperback. Zustand: Very Good. Cover and edges may have some wear. Artikel-Nr. mon0003800491
Anzahl: 1 verfügbar
Beispielbild für diese ISBN
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
Verlag:
Springer (edition Second Edition 2021), 2022
ISBN 10: 1071614207
ISBN 13: 9781071614204
Gebraucht
Paperback
Anbieter: BooksRun, Philadelphia, PA, USA
Verkäuferbewertung 5 von 5 Sternen
Paperback. Zustand: Very Good. Second Edition 2021. Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported. Artikel-Nr. 1071614207-8-1
Anzahl: 2 verfügbar
Foto des Verkäufers
An Introduction to Statistical Learning : with Applications in R
Verlag:
Springer US, Springer New York Jul 2022, 2022
ISBN 10: 1071614207
ISBN 13: 9781071614204
Neu
Taschenbuch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility. Artikel-Nr. 9781071614204
Anzahl: 1 verfügbar
Beispielbild für diese ISBN
An Introduction to Statistical Learning
Verlag:
Springer (India) Private Limited, 2022
ISBN 10: 1071614207
ISBN 13: 9781071614204
Neu
Softcover
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. pp. 607. Artikel-Nr. 402238634
Anzahl: 1 verfügbar
Beispielbild für diese ISBN
AN INTRODUCTION TO STATISTICAL LEARNING
Anbieter: Speedyhen, London, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Zustand: NEW. Artikel-Nr. NW9781071614204
Anzahl: 3 verfügbar
Beispielbild für diese ISBN
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. In. Artikel-Nr. ria9781071614204_new
Anzahl: Mehr als 20 verfügbar