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]
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