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, 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.

统计学习导论》对统计学习领域提供了一个易懂的概述，这是一个重要的工具集，用于理解过去二十年来从生物学、金融学、市场营销到天体物理学等领域出现的庞大而复杂的数据集。本书介绍了一些最重要的建模和预测技术，以及相关应用。主题包括线性回归、分类、重采样方法、收缩方法、基于树的方法、支持向量机、聚类等等。彩色图形和真实世界的例子被用来说明所介绍的方法。由于这本教科书的目标是促进科学、工业和其他领域的从业人员使用这些统计学习技术，因此每一章都包含一个关于在R（一个非常流行的开源统计软件平台）中实现所介绍的分析和方法的教程。其中两位作者共同编写了《统计学习要素》（Hastie, Tibshirani and Friedman, 2009年第二版），这是一本深受统计学和机器学习研究人员欢迎的参考书。统计学习导论》涵盖了许多相同的主题，但其水平却能让更多的读者接受。本书的目标读者是希望使用最先进的统计学习技术来分析其数据的统计学家和非统计学家。该文本只假设以前有线性回归的课程，没有矩阵代数的知识。