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