An Introduction to Statistical Learning with Applications in R (ISL) - Introduction

 

是自己最近学习 "An Introduction to Statistical Learning with Applications in R" 的一个笔记整理。

http://www-bcf.usc.edu/~gareth/ISL/

本书的作者是Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani，发表于February 11, 2013。

此书对统计入门，尤其是监督学习的各种方法，进行了系统性的介绍。更棒的是，每章最后的lab部分，结合了R语言应用实际问题，课后习题中也有专门的R语言练习。

习题的非官方答案可参考 http://blog.princehonest.com/stat-learning/

下面就开始啦~

 

Contents

1. Introduction
2. Statistical Learning: basic terminology, the K-nearest neighbor classifier
3. Linear Regression
4. Classification:logistic regression and linear discriminant analysis (LDA)
5. Resampling Methods: cross-validation and the bootstrap
6. Linear Model Selection and Regularization: stepwise selection, ridge regression, principal components regression, partial least squares, and the lasso.
7. Moving Beyond Linearity: non-linear additive models 
8. Tree-Based Methods: bagging, boosting, and random forests
9. Support Vector Machines
10. Unsupervised Learning: principal components analysis (PCA), K-means clustering, and hierarchical clustering

 

A Brief History of Statistical Learning

• 1800's, method of least squares, linear regression
• 1936, Fisher's linear discriminant analysis (LDA)
• 1940, logistic regression
• 1970's, generalized linear models
• 1980's, classification and regression trees
• 1986, generalized additive models
• today, machine learning