Advanced Topics in Graphcal  Models

10-801, Spring 2007

Prof. Eric P. Xing
School of Computer Science, Carnegie-Mellon University





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Course Description

Probabilistic graphical models provide a succinct formalism with which to describe structured probabilistic models, help unify our understanding of many computational algorithms, and facilitate developing new algorithms and generalizing classical ones to new applications.

This course departs from the basic formalisms/algorithms covered in 10708, and provides an introduction to advanced statistical and computational methods for the modeling and reasoning of complex, multivariate data in large-scale real-world problems. These methods are based on graphical models and other modern methodologies built on Bayesian formalisms, information theory, and optimization techniques. The focus will be on hierarchical Bayesian models, model selection, optimal-margin learning, approximate inference, Bayesian nonparametric methods. We will discuss both the development of theoretical concepts to support such methods and applications of these methods in a number of domains such as information retrieval, NLP, and genetics.

Course requirements

There are three course requirements (there will be no exams):

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