# 机器学习资料

1.Pattern Recognition and machine learning 豆瓣评分9.5

2.Machine Learning: A Probabilistic Perspective 豆瓣评分 9.4

3.统计学习方法 李航 豆瓣评分 8.5

4. Bayesian Reasoning and Machine Learning  豆瓣评分9.3

1和2是机器学习的经典教程，内容很全，值得慢慢精读，3介绍了十种常用机器学习方法，相对简练，支持向量机部分写得很精彩，可以结合1和2阅读；4还没有仔细读，编排顺序和其它区别比较大

1. https://class.coursera.org/ml-003/lecture/index Andrew Ng 非常适合入门

2.http://v.163.com/special/opencourse/machinelearning.html 有点难度，可以在看完1的基础上再看

4 .http://blog.videolectures.net/100-most-popular-machine-learning-talks-at-videolectures-net/ 100 most popular machine learning talks at videolectures.net

2.  http://www.zhizhihu.com/html/y2012/4017.html Max Welling教授笔记

https://news.ycombinator.com/item?id=1055389

1.) Casella, G. and Berger, R.L. (2001). "Statistical Inference" Duxbury Press.

2.) Ferguson, T. (1996). "A Course in Large Sample Theory" Chapman & Hall/CRC.

3.) Lehmann, E. (2004). "Elements of Large-Sample Theory" Springer.

4.) Gelman, A. et al. (2003). "Bayesian Data Analysis" Chapman & Hall/CRC.

5.) Robert, C. and Casella, G. (2005). "Monte Carlo Statistical Methods" Springer.

6.) Grimmett, G. and Stirzaker, D. (2001). "Probability and Random Processes" Oxford.

7.) Pollard, D. (2001). "A User's Guide to Measure Theoretic Probability" Cambridge.

8.) Bertsimas, D. and Tsitsiklis, J. (1997). "Introduction to Linear Optimization" Athena.

9.) Boyd, S. and Vandenberghe, L. (2004). "Convex Optimization" Cambridge.

10.) Golub, G., and Van Loan, C. (1996). "Matrix Computations" Johns Hopkins.

11.) Cover, T. and Thomas, J. "Elements of Information Theory" Wiley.

12.) Kreyszig, E. (1989). "Introductory Functional Analysis with Applications" Wiley.

1. movielens http://www.grouplens.org/datasets/movielens/

2. netflix http://www.datatang.com/data/10135

3. jester http://www.datatang.com/data/13953

4. amazon http://www.datatang.com/data/651

posted @ 2013-09-03 23:33  lijiankou  阅读(560)  评论(0编辑  收藏  举报