先看了简化版 An Introduction to Statistical Learning with Applications in R,内容通俗易懂。然后想再挑战下进阶版The Elements of Statistical Learning (corrected 10th printing),发现数理部份相当难,比如矩阵求导和条件期望,我是非常生疏。

不知道能否看完此书,又需要多长时间。目前想法是做笔记(基本要点),课后习题尽量解,能力有限,肯定错不会少,还请指正。书中部份公式有尝试证明,整理过于耗时,只好先行搁置。

目录

1 Introduction 1

2 Overview of Supervised Learning 9

课后题

3 Linear Methods for Regression 43

4 Linear Methods for Classification 101

5 Basis Expansions and Regularization 139

6 Kernel Smoothing Methods 191

7 Model Assessment and Selection 219

8 Model Inference and Averaging 261

10 Boosting and Additive Trees 337

11 Neural Networks 389

12 Support Vector Machines and Flexible Discriminants 417

13 Prototype Methods and Nearest-Neighbors 459

14 Unsupervised Learning 485

15 Random Forests 587

16 Ensemble Learning 605

17 Undirected Graphical Models 625

18 High-Dimensional Problems: p ≫ N 649