An Introduction to Statistical Learning: With Applications in Python (Paperback)
暫譯: 統計學習導論：Python 應用（平裝）

James, Gareth, Witten, Daniela, Hastie, Trevor

An Introduction to Statistical Learning: With Applications in Python (Paperback)

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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, marketing, and 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. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data.

 

Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R(ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

商品描述(中文翻譯)

統計學習導論（An Introduction to Statistical Learning）以易於理解的方式，概述統計學習（statistical learning）領域。統計學習是理解龐大且複雜資料集不可或缺的一套工具；過去二十年間，這類資料集已出現在從生物學、金融、行銷到天文物理學等各個領域。本書介紹一些最重要的建模與預測技術，以及相關應用。主題包括線性迴歸、分類、重抽樣方法、收縮方法、以樹為基礎的方法、支援向量機、分群、深度學習、生存分析、多重檢定等。書中運用彩色圖表與真實世界的範例，說明所介紹的方法。本書適合統計學家與非統計學家閱讀，特別是希望運用最先進的統計學習技術來分析資料的讀者。

四位作者曾共同撰寫《統計學習導論：R 應用》（An Introduction to Statistical Learning, With Applications in R，ISLR）。這本書已成為全球大學部與研究所課程的重要教材，也是資料科學家的重要參考書。該書成功的關鍵之一，在於每一章都包含教學內容，說明如何在 R 科學計算環境中實作書中介紹的分析方法。然而，近年來 Python 已成為資料科學領域廣受歡迎的語言，因此對以 Python 為基礎、可替代 ISLR 的版本之需求也日益增加。因此，本書（ISLP）涵蓋與 ISLR 相同的內容，但其中的實驗改以 Python 實作。這些實驗不僅適合 Python 初學者，也適合有經驗的使用者。

作者簡介

Gareth James is the John H. Harland Dean of Goizueta Business School at Emory University. 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 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 with that title. Hastie co-developed much of the statistical modeling software and environment in R, and invented principal curves and surfaces. Tibshirani invented the lasso and is co-author of the very successful book, An Introduction to the Bootstrap. They are both elected members of the US National Academy of Sciences.

Jonathan Taylor is a professor of statistics at Stanford University. His research focuses on selective inference and signal detection in structured noise.

 

 

 

作者簡介(中文翻譯)

Gareth James 是 Emory University Goizueta Business School 的 John H. Harland 院長。他在統計學習領域發表了大量方法論研究，尤其著重於高維度資料與函數型資料。本書的概念架構源自他在此領域開設的 MBA 選修課程。

Daniela Witten 是 University of Washington 的統計學與生物統計學教授，以及 Dorothy Gilford 講座教授。她的研究主要聚焦於統計機器學習技術，致力於分析複雜、雜亂且大規模的資料，並特別強調非監督式學習。

Trevor Hastie 與 Robert Tibshirani 是 Stanford University 的統計學教授，也是暢銷教科書《Elements of Statistical Learning》的共同作者。Hastie 與 Tibshirani 開發了廣義加性模型（generalized additive models），並合著了一本以此為書名的廣受歡迎著作。Hastie 共同開發了 R 中大部分的統計建模軟體與環境，並發明了主曲線（principal curves）與主曲面（principal surfaces）。Tibshirani 發明了 lasso，並共同撰寫了廣受好評的《An Introduction to the Bootstrap》。他們兩人都是美國國家科學院（US National Academy of Sciences）院士。

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