【免费下载】 统计学习导论 资源下载

统计学习导论 资源下载

【下载地址】统计学习导论资源下载 统计学习导论 资源下载本仓库提供了《An Introduction to Statistical Learning》(统计学习导论)的资源下载,包括英文版和中文版 【下载地址】统计学习导论资源下载 项目地址: https://gitcode.com/open-source-toolkit/809c4

本仓库提供了《An Introduction to Statistical Learning》(统计学习导论)的资源下载,包括英文版和中文版。这本书是学习机器学习的经典入门书籍,基于R语言的应用,适合初学者阅读和学习。

资源内容

  • 英文版:《An Introduction to Statistical Learning》英文原版PDF文件。
  • 中文版:《统计学习导论》基于R应用的中文翻译版PDF文件。

书籍简介

《An Introduction to Statistical Learning》(统计学习导论)是一本广受欢迎的机器学习入门书籍,由Gareth James, Daniela Witten, Trevor Hastie和Robert Tibshirani合著。书中详细介绍了统计学习的基本概念、方法和应用,并通过丰富的R语言实例帮助读者理解和掌握相关知识。

适用人群

  • 对机器学习感兴趣的初学者。
  • 希望系统学习统计学习理论和方法的学生和研究人员。
  • 需要参考经典教材进行学习和研究的从业者。

使用说明

  1. 下载本仓库中的资源文件。
  2. 打开PDF文件,开始阅读和学习。
  3. 结合书中的R语言实例,进行实践操作,加深理解。

注意事项

  • 本资源仅供学习和研究使用,请勿用于商业用途。
  • 如需引用本书内容,请注明出处。

希望这本书能够帮助你顺利入门机器学习,并在学习和研究中取得进步!

【下载地址】统计学习导论资源下载 统计学习导论 资源下载本仓库提供了《An Introduction to Statistical Learning》(统计学习导论)的资源下载,包括英文版和中文版 【下载地址】统计学习导论资源下载 项目地址: https://gitcode.com/open-source-toolkit/809c4

创作声明:本文部分内容由AI辅助生成(AIGC),仅供参考

Statistical learning refers to a set of tools for modeling and understanding complex datasets. It is a recently developed area in statistics, and blends with parallel developments in computer science, and in particular machine learning. The field encompasses many methods such as the lasso and sparse regression, classification and regression trees, and boosting and support vector machines. With the explosion of “Big Data” problems statistical learning has be- come a very hot field in many scientific areas as well as marketing, finance and other business disciplines. People with statistical learning skills are in high demand. One of the first books in this area — The Elements of Statistical Learn- ing (ESL) (Hastie, Tibshirani, and Friedman) — was published in 2001, with a second edition in 2009. ESL has become a popular text not only in statistics but also in related fields. One of the reasons for ESL’s popu- larity is its relatively accessible style. But ESL is intended for individuals with advanced training in the mathematical sciences. An Introduction to Statistical Learning (ISL) arose from the perceived need for a broader and less technical treatment of these topics. In this new book, we cover many of the same topics as ESL, but we concentrate more on the applications of the methods and less on the mathematical details. We have created labs illustrating how to implement each of the statistical learning methods using the popular statistical software package R . These labs provide the reader with valuable hands-on experience. This book is appropriate for advanced undergraduates or master’s stu- dents in Statistics or related quantitative fields, or for individuals in other disciplines who wish to use statistical learning tools to analyze their data. It can be used as a textbook for a course spanning one or two semesters. We would like to thank several readers for valuable comments on prelim- inary drafts of this book: Pallavi Basu, Alexandra Chouldechova, Patrick Danaher, Will Fithian, Luella Fu, Sam Gross, Max Grazier G’Sell, Court- ney Paulson, Xinghao Qiao, Elisa Sheng, Noah Simon, Kean Ming Tan, Xin Lu Tan. It’s tough to make predictions, especially about the future. -Yogi Berra
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