An Introduction to Statistical Learning 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

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Gareth James 作者

Springer

译者

2013-8-12 出版日期

426 页数

USD 79.99 价格

Hardcover

Springer Texts in Statistics 丛书系列

9781461471370 图书编码

An Introduction to Statistical Learning 在线电子书图书标签: 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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发表于2024-12-22

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An Introduction to Statistical Learning 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

An Introduction to Statistical Learning 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

An Introduction to Statistical Learning 在线电子书用户评价

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Very well written practical overview on statistical pattern recognition. Stay true to the spirit of outlining the essence and not let the mathematical technicalities clutter up discussions. Key themes such as bias-variance trade-off are coherently emphasized throughout. An extremely enjoyable read.

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相比PRML确实是入门级的，配合网上的课件和视频，讲得很清楚，主要针对supervised machine learning

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感觉自己还是学院派，这是截至目前最喜欢的一本机器学习（统计学习）教材，尽管数学原理介绍得也不算深，但总体仍然是重理论、轻代码、轻应用。

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都是算法的介绍和事实结论的堆砌整本书都没有超过两行的技术推导导致很多结论看起来真的有点莫名其妙能够建立/温习基本的框架 ESL的导读版；看的快点两三个星期应该就能挤时间看完了!!

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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。。。

An Introduction to Statistical Learning 在线电子书著者简介

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.

An Introduction to Statistical Learning 在线电子书图书目录

An Introduction to Statistical Learning 在线电子书 pdf 下载 txt下载 epub 下载 mobi 在线电子书下载

An Introduction to Statistical Learning 在线电子书图书描述

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, 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.

An Introduction to Statistical Learning 在线电子书下载 mobi epub pdf txt 在线电子书下载

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An Introduction to Statistical Learning 在线电子书读后感

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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...

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1，统计学习的入门书，通俗易懂，号称是ESL的入门版，全书没有太多数学推导，适合学工程的人不适合学统计的人读。2，监督学习占了大部分篇幅，我觉得这本书最好的部分就是模型的讨论都围绕variance和bias的trade-off展开，还有就是对模型的整体性能，以及参数的经验取值都给出...

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1. expected test MSE use：to assess the accuracy of model predictions. obtain: repeatedly estimate f using a large number of training sets and test each at x0. decompose: into 3 parts -- variance, bias and irreducible error. note: the meaning of variance an...

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

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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/

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An Introduction to Statistical Learning 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024