Introduction to Artificial Intelligence: Probabilistic Reasoning and Decision-Making

4 credits

Introduction to probabilistic models at the heart of modern artificial intelligence. Specific topics to be covered include probabilistic methods for reasoning and decision-making under uncertainty; inference and learning in Bayesian networks; prediction and planning in Markov decision processes; applications to intelligent systems, speech and natural language processing, information retrieval, and robotics. Prerequisites: CSE 12 or DSC 40B and CSE 15L or CSE 29 or DSC 80 and COGS 118D or CSE 103 or ECE 109 or ECON 120A or MAE 108 or MATH 180A or MATH 180B or MATH 181A or MATH 183 or MATH 186 and MATH 20A and MATH 18 or MATH 31AH; restricted to students within the CS25, CS26, CS27, CS29, EC26, and DS25 majors. All other students will be allowed as space permits.