Faculty Candidate Seminar

Game Trees

Pat VirtuePh.D. CandidateUniversity of California Berkeley

CSE Lecturer Candidate Seminar
From checkers and chess to Go and Dota, games have played a central role throughout the history of artificial intelligence. In this lecture, we focus on how we can adapt classical minimax tree search to handle uncertainty in both games and the real world. Students will learn the mathematical notation associated with these recursive methods, which will ease the transition to future AI topics, such as reinforcement learning. The material in this lecture is appropriate for the beginning of an introductory AI course or as a fun way to combine probability and tree structures in an introductory computer science course.
Pat Virtue is a Ph.D. candidate in EECS at the University of California, Berkeley, specializing in deep learning, computer vision, and medical imaging. He teaches the Introduction to Artificial Intelligence course at UC Berkeley, and prior to graduate school, he designed medical image applications as a software engineer at GE Healthcare.

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