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2024-05-18

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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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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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拯救看不懂ESL的学渣们所写的一本书，作者着实佛心

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理论解释非常到位，但需要结合code与case study来消化吸收，应用

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拯救看不懂ESL的学渣们所写的一本书，作者着实佛心

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非常好的教材！写得极为清晰，例子也很好。这是迄今为止第一本让我有愉悦体验的统计类教材。

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. 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☆☆☆☆☆

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。

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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...

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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...

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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