An Introduction to Statistical Learning: With Applications in R, 2/e (Hardcover)
暫譯: 統計學習導論：R語言應用實例，第2版（精裝本）

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》提供了統計學習領域的易懂概述，這是一套對於理解過去二十年在生物學、金融、行銷、天體物理等領域出現的龐大且複雜數據集至關重要的工具。本書介紹了一些最重要的建模和預測技術，以及相關的應用。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、多重測試等。書中使用彩色圖形和真實世界的例子來說明所介紹的方法。由於這本教科書的目標是促進科學、工業及其他領域的從業者使用這些統計學習技術，因此每一章都包含了在 R 這個極受歡迎的開源統計軟體平台上實施所介紹的分析和方法的教程。

兩位作者共同撰寫了《統計學習的元素》（Hastie, Tibshirani 和 Friedman，第二版 2009），這是一本受歡迎的統計學和機器學習研究者的參考書。《統計學習導論》涵蓋了許多相同的主題，但以更易於更廣泛讀者群的方式呈現。本書的目標讀者是希望使用尖端統計學習技術來分析數據的統計學家和非統計學家。文本僅假設讀者具備線性回歸的先修課程，並不需要具備矩陣代數的知識。

本第二版新增了有關深度學習、生存分析和多重測試的新章節，並擴展了對朴素貝葉斯、廣義線性模型、貝葉斯加法回歸樹和矩陣補全的處理。R 代碼已全面更新，以確保相容性。

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)

加雷斯·詹姆斯是南加州大學數據科學與運營的教授，以及商業管理的E.摩根·史丹利講座教授。他在統計學習領域發表了大量的方法論研究，特別強調高維度和函數數據。本書的概念框架源自他在該領域的MBA選修課程。

丹妮拉·維滕是華盛頓大學統計學和生物統計學的教授，以及多蘿西·吉爾福德捐贈講座教授。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，特別強調無監督學習。

特雷弗·哈斯提和羅伯特·提布希拉尼是斯坦福大學的統計學教授，也是成功教科書《統計學習要素》的共同作者。哈斯提和提布希拉尼開發了廣義加法模型，並撰寫了同名的熱門書籍。哈斯提共同開發了R/S-PLUS中的許多統計建模軟體和環境，並發明了主曲線和主表面。提布希拉尼提出了套索（lasso），並是非常成功的《自助法導論》的共同作者。

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