An Introduction to Statistical Learning pdf epub mobi txt 電子書下載 2024

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Gareth James

Springer

2013-8-12

426

USD 79.99

Hardcover

Springer Texts in Statistics

9781461471370

圖書標籤: 機器學習統計學習 R 統計數據分析 Statistics 統計學 machine_learning

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发表于2024-11-22

An Introduction to Statistical Learning epub 下載 mobi 下載 pdf 下載 txt 電子書下載 2024

An Introduction to Statistical Learning pdf epub mobi txt 電子書下載 2024

圖書描述

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 下載 mobi epub pdf txt 電子書

著者簡介

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.

圖書目錄

An Introduction to Statistical Learning pdf epub mobi txt 電子書下載

想要找書就要到小哈圖書下載中心

qciss.net

立刻按 ctrl+D收藏本頁

你會得到大驚喜!!

用戶評價

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簡明清晰，對於常用的方法基本都有涉獵。對讀者的知識背景沒太多要求，所以也很難深入。差不多是當成復習+加深印象。本來是想讀elements那本，可是綫性代數忘光瞭，看著矩陣證明真想以頭搶地T-T

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非常好的教材！寫得極為清晰，例子也很好。這是迄今為止第一本讓我有愉悅體驗的統計類教材。

評分☆☆☆☆☆

理論解釋非常到位，但需要結閤code與case study來消化吸收，應用

評分☆☆☆☆☆

ISLR在機器學習界大名鼎鼎，個人認為是最適閤初級學習者的著作。雖說是ESLR的簡化版，但是精華該有的都有，全書脈絡清晰無比，從Bias-Variance Tradeoff和No Free Lunch兩條基本思想展開，作者的深厚統計學背景使得LogReg、PCA和LDA這些概念主題都能有一個清楚的闡釋。以理論為主，但是也有lab，方便讀者動手一窺究竟。這本書甚至激起瞭我的一點學習數學的心情，接下來打算用Strang的那本綫代和Casella的統計推斷好好鞏固基礎，屆時再迴味想必又能有新的體會。Logistic和SVM等部分讀起來一氣嗬成，真可謂“清水齣芙蓉”，而對模型的討論始終堅持問題導嚮，有一些哲學思維。唯一的遺憾就是預期讀者的數學水平掣肘瞭內容的發揮。

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讀後感

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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...

評分☆☆☆☆☆

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...

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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...

評分☆☆☆☆☆

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...

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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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An Introduction to Statistical Learning pdf epub mobi txt 電子書下載 2024