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CS B553: Algorithms for Optimization and Learning

Spring 2012

Instructor: Kris Hauser

 

The goal of this course is to provide students with an understanding of foundational computational techniques for large-scale optimization and probabilistic inference, which find broad application in the advanced study of artificial intelligence, robotics, computer vision, computational biology, and applied sciences.

Optimization

·                  Unconstrained optimization: gradient descent, Newton and quasi-Newton methods

·                  Constrained optimization: Lagrange duality, KKT conditions, convex optimization, nonlinear programming

·                  Stochastic optimization: simulated annealing, genetic algorithms, stochastic gradient descent.

Learning

·         Bayesian inference, Monte-Carlo techniques

·         Graphical models: Bayes nets.  Exact and approximate inference.  Parameter & structure learning.

·         Expectation-Maximization

·         Temporal sequence processing: Markov chains, HMM, Viterbi algorithm, particle filtering