书籍介绍
This book is our attempt to bring together many of the important new ideas in learning, and explain them in a statistical framework. While some mathematical details are needed, we emphasize the methods and their conceptual underpinnings rather than their theoretical properties. As a result, we hope that this book will appeal not just to statisticians but also to researchers and practitioners in a wide variety of fields.
AI导读
核心看点
- 系统阐述统计学习框架下的机器学习方法
- 强调算法概念理解而非纯理论推导
- 涵盖回归、分类、树模型及神经网络等
适合谁读
- 具备扎实线性代数与概率统计基础者
- 统计学、计算机科学及相关领域研究生
- 希望深入理解算法原理的研究人员
读前提醒
- 非入门书，需具备较强数学功底
- 建议搭配入门教材或查阅论文辅助
- 可访问官网获取免费电子版资源
读者共识
- 内容艰深，被戏称为从入门到放弃
- 经典权威，但阅读门槛极高
- 适合精读或作为工具书查阅参考
本导读基于书籍简介、目录、原文摘录、短评和书评生成，不等同于全文精读。
精彩摘录
- "LAR uses least squares directions in the active set of variables. Lasso uses least square directions; if a variable crosses zero, it is removed from the active set. Boosting uses non-negative least squares directions in the active set."
- "the bias of the 1-nearest-neighbor estimate is often low, but the variance is high."
- "Monte Carlo is an extremely bad method; it should be used only when all alternative methods are worse"
- "有一个关于Metropolis算法的故事，非常流行：一晚，Edward、Metropolis和Marshall在派对上讨论这个问题，在鸡尾酒餐巾纸上写出了这个闻名的算法。他们最终的论文之所以写上妻子的名字，是为了安抚被整晚的技术性讨论所烦扰的女人Arianna和Augusta"
- "Bagging; or Bootstrap AGGregatING, is an extension of bootstrapping to classification and regression problems. The main idea is to sample with replacement from the training data so that we now have B training data sets, each having n′≤n observations. The machine-learning algorithm is trained on each"
- "还是觉得对这本书相见恨晚，研一写那么都web app有毛用啊，就应该踏踏实实的多读书啊~ 还好去实习了，还好发现原来啥都不知道，还好坚持把这本书啃完了，虽然理解的较为粗陋，要不要去读个博呢......真苦恼~"
- "Both k-nearest neighbors and least squares end up approximating conditional expectatios by averages."
- "However, with a 0 − 1 outcome, this computation simplifies. We order the predictor classes according to the proportion falling in outcome class 1. Then we split this predictor as if it were an ordered predictor."
目录
Preface to the Second Edition
Preface to the First Edition
1 Introduction
2 Overview of Supervised Learning
3 Linear Methods for Regression
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用户评论
Good Introduction with detailed explanation in spite of tediousness.
太牛批了 有些算法虽然已经了解过 但是从没想过居然会有那么深的理论基础 每次阅读都有一种 哇！这个居然这么精妙 的感觉
哼，说好的基础呢！！！一点都不基础，看得我累死了，然后放弃了
好书就得品着读
一直觉得这个学科大概不存在入门教材这个概念。
ESL：ML专著。可翻
讨论班一直在讲这本书，难度对我来说挺高的，但是读透了就会有种醍醐灌顶的感觉，机器学习的必读书目之一吧。
我来说下，本书面向的读者对象为精通概率统计的人，即你差不多是个统计学博士就可以了，不然很多结论直接就来，也不推导。本书重概率统计直觉，我觉得此书很尴尬，厉害的人觉得就是个提纲，水平差的人又觉得太跳跃。最后，统计机器学习入门慎看此书。
名气很大，内容很散，不如直接读论文
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