An Introduction to Statistical Learning: With Applications in R, 2/e (Hardcover) (統計學習導論：R語言應用實例（第二版）)

James, Gareth, Witten, Daniela, Hastie, Trevor

An Introduction to Statistical Learning: With Applications in R, 2/e (Hardcover)

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商品描述

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 na ve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

 

商品描述(中文翻譯)

《統計學習導論》提供了對統計學習領域的易於理解的概述，這是一個在過去二十年中在生物學、金融、市場營銷和天體物理學等領域中出現的龐大而複雜的數據集的必備工具集。本書介紹了一些最重要的建模和預測技術，以及相關應用。主題包括線性回歸、分類、重抽樣方法、收縮方法、基於樹的方法、支持向量機、聚類、深度學習、生存分析、多重檢驗等。使用彩色圖形和實際示例來說明所介紹的方法。由於本教科書的目標是幫助科學、工業和其他領域的從業人員使用這些統計學習技術，因此每章都包含了在R中實施分析和方法的教程，R是一個非常流行的開源統計軟件平台。

本書的兩位作者之一與Hastie、Tibshirani和Friedman合著了《統計學習的要素》（第二版，2009年），這是一本統計學和機器學習研究人員廣泛參考的書籍。《統計學習導論》涵蓋了許多相同的主題，但面向的受眾更廣泛。本書針對統計學家和非統計學家，他們希望使用尖端的統計學習技術來分析他們的數據。本書假設只有一門線性回歸的先修課程，並且不需要矩陣代數的知識。

第二版新增了關於深度學習、生存分析和多重檢驗的章節，並對naïve Bayes、廣義線性模型、貝葉斯加法回歸樹和矩陣補全進行了擴展。R代碼已經更新，以確保兼容性。

作者簡介

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.

作者簡介(中文翻譯)

Gareth James是南加州大學的數據科學和運營學教授，也是E. Morgan Stanley商業管理講座教授。他在統計學習領域發表了大量的方法論研究，尤其關注高維和函數數據。這本書的概念框架源於他在MBA選修課程中的教學內容。

Daniela Witten是華盛頓大學的統計學和生物統計學教授，也是Dorothy Gilford榮譽講座教授。她的研究主要集中在統計機器學習技術，用於分析複雜、混亂和大規模數據，尤其關注無監督學習。

Trevor Hastie和Robert Tibshirani是斯坦福大學的統計學教授，也是《統計學習的要素》一書的合著者。Hastie和Tibshirani開發了廣義加法模型並撰寫了一本廣受歡迎的同名書籍。Hastie共同開發了R/S-PLUS統計建模軟件和環境，並發明了主曲線和曲面。Tibshirani提出了套索方法，並是《引薦Bootstrap》一書的合著者。

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