Probabilistic Graphical Models
10-708, Fall 2007
Many of the problems in artificial intelligence, statistics, computer systems, computer vision, natural language processing, and computational biology, among many other fields, can be viewed as the search for a coherent global conclusion from local information. The probabilistic graphical models framework provides an unified view for this wide range of problems, enabling efficient inference, decision-making and learning in problems with a very large number of attributes and huge datasets. This graduate-level course will provide you with a strong foundation for both applying graphical models to complex problems and for addressing core research topics in graphical models.
The class will cover
aspects: The core representation, including Bayesian and Markov
and dynamic Bayesian networks; probabilistic inference
algorithms, both exact and approximate; and, learning methods for both
parameters and the structure of graphical models. Students entering the
should have a pre-existing working knowledge of probability,
algorithms, though the class has been designed to allow students with a
background to catch up and fully participate.
It is expected that after taking this class, the students should have obtain sufficient working knowledge of multi-variate probablistic modeling and inference for practical applications, should be able to fomulate and solve a wide range of problems in their own domain using GM, and can advance into more specialized technical literature by themselves.
Students are required to have successfully completed 10701/15781, or an equivalent class.