An Introduction to Statistical Learning

Gareth James is a professor of data sciences and operations 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 an associate professor of statistics and biostatistics at the University of Washington. Her research focuses largely on statistical machine learning in the high-dimensional setting, 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.

出版者:Springer

作者:Gareth James

页数:426

出版时间:2013-8-12

价格:USD 79.99

装帧:Hardcover

isbn号码:9781461471370

丛书系列:Springer Texts in Statistics

图书标签:机器学习统计学习R统计数据分析Statistics统计学machine_learning

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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.

读后感

Notes of Introduction to Statistical Learning ===================================== ## Statistical Learning - basic concepts - two main reasons to estimate f: prediction and inference - trade-off: complex models may be good for accurate prediction, but it m...

业界良心，为学渣精心打造……深入浅出，甚至连矩阵怎么算怕你不会都告诉你，而且尽量避免使用矩阵之类的纯数学的表达，比较适合只学习应用的同学，不用关心太多内在证明。例子给的也很足，非常实际。R的例子讲的也很实用。总之非常适合自学。  

这本书读起来不费劲，弱化了数学推导过程，注重思维的直观理解和启发。读起来很畅快，个人感觉第三章线性回归写的很好，即使是很简单的线性模型，作者提出的几个问题和细细的解释这些问题对人很有启发性，逻辑梳理得很好，也易懂。(不过有点可惜的是翻译版本确实不是太好，有些...  

Notes of Introduction to Statistical Learning ===================================== ## Statistical Learning - basic concepts - two main reasons to estimate f: prediction and inference - trade-off: complex models may be good for accurate prediction, but it m...

很适合入门，几乎没有什么数学，英文读起来也很简单，一些词汇不懂可以对照中文版。中文版叫：统计学习导论：基于 R 应用。适合刚刚接触机器学习的同学阅读。和适合我这种菜鸟阅读学习，下载了 N 本机器学习的书了，这本是唯一能读的下去的。初学主要是先了解概念，对机器学习...  

用户评价

公开课的教材，没涉及太多的数学，不错。https://class.stanford.edu/courses/HumanitiesScience/StatLearning/Winter2014/courseware/dfece96897994039a17547b575573447/

http://www-bcf.usc.edu/~gareth/ISL/

终于读完了。没有用 R ，还是偷懒用了 Python 的 scikit learn，基本差不多。

写得这么好的教材竟然还不要钱！业界良心啊~ 唯一的缺点是有点啰嗦……

终于读完了。没有用 R ，还是偷懒用了 Python 的 scikit learn，基本差不多。