Statistical Learning: Modeling, Prediction and Computing
STAT 538 Winter Quarter 2011


Course Description

Books and other resources

Class mailing list




Handouts/Course notes

UW Statistics

UW Biostatistics


  • Supervised and semisupervised learning (1-2 lectures)
    • an overview
    • Classification: generative vs. discriminative models
    • Basic theorems
    • Where does optimization fit in?
  • Unconstrained optimization (3 lectures)
  • Boosting as gradient descent (2 lectures)
  • Convex sets and functions. Examples from statistics. (4 lectures)
    • Entropy and information.
    • exponential family models, the maximum entropy principle, Bregman divergences (applications to clustering)
    • [time permitting] principles of approximate inference in graphical models
  • Convex constrained optimization problems and duality (3 lectures)
  • Support vector machines as convex optimization problems (3 lectures)
  • Compressed sensing, the LASSO and l1 regularization (2-3 lectures)
  • Algorithms for constrained optimization (1-2 lectures)
  • [time permitting] Conic programming, semidefinite programming and applications to modern kernel learning algorithms

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