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.
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