04-654 Introduction to Probabilistic Graphical Model

12 units, Carnegie Mellon University

TThis course provides an introduction to the subject of Probabilistic Graphical Models PGM. PGM give a unified view for a wide range of problems arising in several domains such as artificial intelligence, statistics, computer systems, computer vision, natural language processing, and computational biology, among many other fields. They provide a very flexible and powerful framework for capturing statistical dependencies in complex, multivariate data. PGM brings together probability theory and graph theory to enable efficient inference, decisionmaking, and learning in problems with a very large number of attributes and huge datasets. This introductory course will provide you with a strong foundation necessary for applying graphical models to complex problems.

Prerequisites: none listed

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