DS 6234

Course Description

Covers the fundamental concepts of uncertainty in artificial intelligence (AI). Students will explore various techniques and models used to handle uncertainty in AI and machine learning systems, including Bayesian deep learning, dropout as a Bayesian approximation, and decision theory. Will also cover applications of uncertainty in AI, such as computer vision, natural language processing,and autonomous systems.