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

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Gareth James

Springer

2013-8-12

426

USD 79.99

Hardcover

Springer Texts in Statistics

9781461471370

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2024-10-03

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

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

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 下载 mobi epub pdf txt 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.

图书目录

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

想要找书就要到小哈图书下载中心

qciss.net

立刻按 ctrl+D收藏本页

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用户评价

评分☆☆☆☆☆

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。

评分☆☆☆☆☆

简明清晰，对于常用的方法基本都有涉猎。对读者的知识背景没太多要求，所以也很难深入。差不多是当成复习+加深印象。本来是想读elements那本，可是线性代数忘光了，看着矩阵证明真想以头抢地T-T

评分☆☆☆☆☆

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

评分☆☆☆☆☆

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

评分☆☆☆☆☆

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/

读后感

评分☆☆☆☆☆

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。

评分☆☆☆☆☆

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...

评分☆☆☆☆☆

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...

评分☆☆☆☆☆

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...

评分☆☆☆☆☆

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 pdf epub mobi txt 电子书下载 2024