Fall 2017, 2018
Derek Bok Center Distinction in Teaching Award
Fall 2017, 2018
Artificial Intelligence (AI) is an exciting field that has enabled a wide range of cutting-edge tech-nology, from driverless cars to grandmaster-beating Go programs. The goal of this course is to introduce the ideas and techniques underlying the design of intelligent computer systems. Topics covered in this course are broadly be divided into 1) planning and search algorithms, 2) probabilistic reasoning and representations, and 3) machine learning (although, as you will see, it is impossible to separate these ideas so neatly). Within each area, the course will also present practical AI algorithms being used in the wild and, in some cases, explore the relationship to state-of-the-art techniques. The class will include lectures connecting the models and algorithms we discussto applications in robotics, computer vision, and related domains. Specific topics covered include classical graph search methods, constraint satisfaction problems, Markov decision processes, reinforcement learning, robot motion planning, probability theory, Bayes nets, hidden Markov models, filtering, basic optimization, classification, and regression. The course will provide a good foundation for topics covered in advanced AI courses (CS28x). CS 182 complements CS 181, which emphasizes machine learning. Students who take both CS 182 and CS 181, will have a solid background for understanding and contextualizing modern AI research and experience implementing algorithms in several key areas of the field. Finally, in spite of its practical usefulness this course is also quite fun. AI also has a long historyof research into topics like puzzle-solving, game-playing, robotics, and conversational chat-bots. In this spirit, problem sets will include programming intelligent Pac-Man agents and simple simulated robots.