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。
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