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