The course will introduce the basic ideas and techniques underlying the design and learning of intelligent machines. By the end of this course, you will have learned how to build autonomous (software) agents that efficiently make decisions in fully informed, partially observable and adversarial settings as well as how to optimize actions in uncertain sequential decision making environments to maximize expected reward.
Constraint Satisfaction Problems
Bayes Decision Theory / Classifiers
Markov Decision Processes