Indiana University Bloomington

Computer Science B554
Probabilistic Approaches To Artificial Intelligence

Contact: Martha White
Offered: Spring, 2015
Capacity: 50
Pre-Requisites: CSCI-B551 or permission of the instructor.
Instructor: Martha White
Days Per Week Offered: Two.
Syllabus: No Syllabus Avaliable
Description: Topics will include:
  • Review of probability theory and basic calculus
  • Graphical model frameworks: Bayes networks, Markov networks
  • Exact inference: Variable elimination, conditioning, clique trees
  • Approximate inference: Belief propagation, graph cuts, particle-based inference
  • Inference as optimization
  • Optimization techniques: Gradient descent, Newton methods, constrained optimization, stochastic optimization, genetic algorithms - Learning: maximum likelihood and MAP parameter estimation, structure learning, Expectation-Maximization
  • Temporal models: Markov chains, hidden Markov models
  • Applications.
  • Books: Koller and Friedman, Probabilistic Graphical Models, MIT Press, 2009. We will also read research papers and selected chapters from other books.
    Applied/Theoretical: Balanced.
    Formal Computing Lab: No
    Problem Sets: Approximately 6 assignments, a final project, and occasional in-class quizzes. The assignments will include both programming and pen-and-paper problems.
    Comments: The course will require some level of mathematical maturity, especially with linear algebra, probability theory, and basic calculus, although we will review the key mathematical concepts.