 Supervised and semisupervised learning (12 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 (23 lectures)
 Algorithms for constrained optimization (12 lectures)
 [time permitting] Conic programming, semidefinite programming and applications to modern kernel learning algorithms
