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In The Course We Will Investigate Probabilistic Graphical Models (pgms) Which Combine Graph Theory and Probability Theory to Provide A Flexible Framework For Modelling Large Multivariate Distributions With Complex Interactions Between Its Random Variables. Pgms Have Become a Central Tool in a Wide Range of Applications That Include Natural Language Processing, Computer Vision, Computational Biology, And More. We We Learn How to Use Pgms to Compactly Represent Complex Systems and Perform Sophisticated Reasoning Tasks. in This Course We Will Cover The Fundamental Principles of Pgms That Include Knowledge Representation, Query Answering (inference), and Learning The Parameters and Structure of a Pgm From Data. Learning Outcomes# At The End of The Course The Students Will Be Able To# 1. Represent Multivariate Probability Distributions in a Pgm. 2. Query The Pgm and Analyze The Computational Complexity Involved. 3. Learn The Paarameters and Structure of a Pgm From Data.

Faculty: Data and Decision Sciences
|Undergraduate Studies |Graduate Studies

Pre-required courses

(94224 - Data Structures and Algorithms and 96411 - Machine Learning 1)