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

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

其实我最大的感触是书中总是说某某内容 “ is beyond the scope of this book” ，真是难为几位作者了。 --------------------------- 高清无码图见相册： https://www.douban.com/photos/photo/2462258822/  

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...  

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...

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

其实我最大的感触是书中总是说某某内容 “ is beyond the scope of this book” ，真是难为几位作者了。 --------------------------- 高清无码图见相册： https://www.douban.com/photos/photo/2462258822/  

用户评价

statistical learning的入门级教材，不需要很多的数学，但涵盖了许多topic，而且每章结尾都有R的实例，不过这本书还是过于基础，unsupervised learning只有一章，而且居然跳过了neural networks。

果然是element of statistical learning的R语言简明版。或者看成ESL的导读也行。

好书！另此书又名“给学渣的机器学习书”。。。。

是写的很好，常用的基础算法里面缺了神经网络，不过光看这本也是远远不够的。。。

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