Bayesian Artificial Intelligence. 3 credits

Catalog description

Many artificial intelligence problems involve modeling uncertainty. Bayesian probabilistic models represent uncertainty and dependencies between random variables using probability distributions. You will learn the set of rules of probability and computational algorithms to manipulate these distributions. Bayesian approach enhances the effectiveness of conventional AI techniques. This course summarizes various Bayesian-based models and the standard algorithms used with them, supplemented by instances of their practical use. We will discuss applications in science, engineering, economics, medicine, sport, and law. Students will learn the commonalities and differences between the Bayesian and frequentist approaches to statistical inference, how to approach a statistics problem from the Bayesian perspective, and how to combine data with informed expert judgment soundly to derive useful and policy-relevant conclusions. Assignments focus on applying the methods to practical problems. Offered by Systems Engr & Operations Rsch . May not be repeated for credit. Equivalent to CSI 674 , SYST 664 .