伯克利大学机器学习(Practical Machine Learning)
CS 294-34: Practical Machine Learning

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