Course Description
Algorithms for creating computer programs that can improve their performance through learning.
Topics include: cross-validation, decision trees, neural nets, statistical tests, Bayesian learning, computational learning theory, instance-based learning, reinforcement learning, bagging, boosting, support vector machines,
Hidden Markov Models, clustering, and semi-supervised and unsupervised learning techniques.