B554 Probabilistic Approaches to Artificial Intelligence
Theory and practice of computational and mathematical foundations of probabilistic models for artificial intelligence and other areas of computing. Topics include: random variables and independence; graphical models including Bayesian and Markov networks; exact and approximate inference algorithms; constrained, unconstrained and stochastic optimization algorithms; parameter and structure estimation; temporal models; applications.
Instructor: Martha White
Time: 1:00PM-2:15PM Mon, Wed
Location: Lindley Hall, Room 008
Course URL (syllabus link or course homepage)
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