Description
To provide a comprehensive introduction to current research methods in artificial intelligence. The course is appropriate both for nonspecialists who wish to acquire a strong grounding in the engineering aspects of computing with real-world data, and as a prerequisite to more advanced courses in artificial intelligence.
Douglas DeCarlo, Haym Hirsh, Casimir Kulikowski, L. Thorne McCarty, Vladimir Pavlovic, Chung-chieh Shan, Louis Steinberg, Matthew Stone
Credits: 3
Category: B
Prerequisites: An undergraduate course in artificial intelligence.
Semesters Offered:Fall
Topics: Modeling structure and uncertainty in real-world data and reasoning with these models, including perception, categorization and learning. Applications to text and web processing, computer vision, speech recognition and language processing, and user interfaces. Planning for risk and reward. Applications to expert systems, medical decision-making, robotics and design.
Expected Work: Midterm and final examinations and occasional readings, problem sets, and programming assignments.
