Northwestern University 1 credit Computer Science
Probabilistic graphical models are a powerful technique for handling uncertainty in machine learning. The course will cover how probability distributions can be represented in graphical models, how inference and learning are performed in the models, and how the models are utilized for machine learning in practice. Prerequisites: COMP_SCI 349 or PhD CS or permission of the instructor.
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