Practical Machine Learning

Link from : http://www.cvchina.info/2010/11/03/practical-machine-learning/

This course introduces core statistical machine learning algorithms in a (relatively) non-mathmatical way, emphasizing applied problem-solving. The prerequisites are light; some prior exposure to basic probability and to linear algebra will suffice.

Jan 22: Tutorial [Ariel Kleiner]

Jan 29: Classification [Simon Lacoste-Julien]

Feb 5: Regression [Romain Thibaux]

Feb 12: Clustering [Sriram Sankararaman]

Feb 19: Dimensionality reduction [Percy Liang]

Feb 26: Feature selection [Alex Bouchard]

Mar 4: Cross-validation, bootstrap, ROC plots [Gad Kimmel]

Mar 11: Hidden Markov models, graphical models [Erik Sudderth]

Mar 18: Visualization and nonlinear dimensionality reduction [Fei Sha]

Apr 1: Collaborative filtering [Alex Simma]

Apr 8: Reinforcement learning [Peter Bodik]

Apr 15: Time series, sequential hypothesis testing, anomaly detection [Charles Sutton]

Apr 22: Nonparametric Bayesian methods (Dirichlet processes) [Kurt Miller]

Apr 29: Active learning, experimental design [Alex Shyr]

May 6: Multi-class classification, structured classification [Guillaume Obozinski]