An Introduction to Statistical Learning 2025 pdf epub mobi 电子书

☆☆☆☆☆
简体网页||繁体网页

An Introduction to Statistical Learning 2025 pdf epub mobi 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 电子书图书目录

下载链接在页面底部

下载链接1
下载链接2
下载链接3

facebook linkedin mastodon messenger pinterest reddit telegram twitter viber vkontakte whatsapp 复制链接

想要找书就要到本本书屋

onlinetoolsland.com

立刻按 ctrl+D收藏本页

你会得到大惊喜!!

发表于2025-03-04

An Introduction to Statistical Learning 2025 pdf epub mobi 电子书

喜欢 An Introduction to Statistical Learning 电子书的读者还喜欢

An Introduction to Statistical Learning 电子书读后感

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☆☆☆☆☆

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...

评分☆☆☆☆☆

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

评分☆☆☆☆☆

类似图书点击查看全场最低价

出版者:Springer

作者:Gareth James

出品人:

页数:426

译者:

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 2025 pdf epub mobi 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 2025 pdf epub mobi 电子书

想要找书就要到本本书屋

onlinetoolsland.com

立刻按 ctrl+D收藏本页

你会得到大惊喜!!

An Introduction to Statistical Learning 2025 pdf epub mobi 用户评价

评分☆☆☆☆☆

理论解释非常到位，但需要结合code与case study来消化吸收，应用

评分☆☆☆☆☆

ISLR在机器学习界大名鼎鼎，个人认为是最适合初级学习者的著作。虽说是ESLR的简化版，但是精华该有的都有，全书脉络清晰无比，从Bias-Variance Tradeoff和No Free Lunch两条基本思想展开，作者的深厚统计学背景使得LogReg、PCA和LDA这些概念主题都能有一个清楚的阐释。以理论为主，但是也有lab，方便读者动手一窥究竟。这本书甚至激起了我的一点学习数学的心情，接下来打算用Strang的那本线代和Casella的统计推断好好巩固基础，届时再回味想必又能有新的体会。Logistic和SVM等部分读起来一气呵成，真可谓“清水出芙蓉”，而对模型的讨论始终坚持问题导向，有一些哲学思维。唯一的遗憾就是预期读者的数学水平掣肘了内容的发挥。

评分☆☆☆☆☆

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

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☆☆☆☆☆

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

An Introduction to Statistical Learning 2025 pdf epub mobi 电子书