伯克利大学“机器学习(Practical Machine Learning)"课件及相关资料

原文在这里：http://www.cnblogs.com/scnucs/archive/2012/09/29/2708790.html

Cousera上不去了，我们就看课件吧。伯克利大学的"Practical Machine Learning”课程，用Google翻译称之为“实用机器学习”，不能拍板这样翻译是否合适，就省略了前两个字。注意这个课不是Coursera上的，是伯克利自己的CS课，由大名鼎鼎的Michale Jordan教授主持，多位老师来授课，虽然没有视频，但是课件还是挺详细的，大家点击下面的链接后会有每节课相关的课件链接：

Lectures (Tentative Schedule)

Aug 27: Tutorial [Ariel
Kleiner]
Sep 3: Classification [Michael
Jordan]
Sep 10: Regression [Fabian
Wauthier]
Sep 17: Clustering [Sriram
Sankararaman]
Sep 24: Dimensionality reduction [Percy
Liang]
Oct 1: Feature selection [Alex
Bouchard]
Oct 8: Hidden Markov models, graphical
models [Alex Simma]
Oct 15: Collaborative Filtering [Lester
Mackey]
Oct 22: Active learning, experimental
design [Daniel Ting]
Oct 29: Reinforcement learning [Peter
Bodik]
Nov 5: Bootstrap, cross-validation,
ROC plots [Michael Jordan]
Nov 12: Time series, sequential hypothesis
testing, anomaly detection [Alex Shyr]
Nov 19: Bayesian nonparametric
methods (Dirichlet processes) [Kurt Miller]
Dec 3: Optimization methods for
learning [John Duchi]

课程其他资料请参考其主页：Computer Science 294 Practical Machine Learning