Graduate

Courses

Computer Science

B554 Probabilistic Approaches to Artificial Intelligence

Credits: 3

Prerequisite(s): MATH-M 365, MATH-M 301 and CSCI-B 403.

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.

  • Course History

      Spring 2015


      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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