Syllabus
Uncertainty reasoning: probabilistic methods, Bayesian networks, Markov models.
Decision making: utility theory, Markov Decision Processes, decisions with multiple agents, (inverse) game theory.
Machine learning: supervised learning, decision trees, regression, SVM, boosting; version space search; learning probabilistic models, the EM algorithm; reinforcement learning.
Annotation
The course covers uncertainty in artificial intelligence, decision making, and basic methods of machine learning.