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CS 838 - Uncertain Reasoning in Al
Advances in using uncertain knowledge to make decisions rationally and effectively (for prediction, classification, diagnosis, etc.). Focus is placed on modelling with Bayesian networks and various methods for inference therein. Deep learning probabilistic circuits, including arithmetic circuits and sum-product networks, will be extensively studied, emphasizing construction, learning, semantics, and inference. Note: Before registering for this course, students should have a background in discrete computational structures, artificial intelligence and statistical methods comparable to the senior undergraduate level. 3.000 Credit hours 3.000 Lecture hours Levels: Graduate Schedule Types: Lecture Computer Science Department Restrictions: Must be enrolled in one of the following Levels: Graduate

CS 838 - Uncertain Reasoning in Al

Advances in using uncertain knowledge to make decisions rationally and effectively (for prediction, classification, diagnosis, etc.). Focus is placed on modelling with Bayesian networks and various methods for inference therein. Deep learning probabilistic circuits, including arithmetic circuits and sum-product networks, will be extensively studied, emphasizing construction, learning, semantics, and inference. *Note: Before registering for this course, students should have a background in discrete computational structures, artificial intelligence and statistical methods comparable to the senior undergraduate level.*
3.000 Credit hours
3.000 Lecture hours

Levels: Graduate
Schedule Types: Lecture

Computer Science Department

Restrictions:
Must be enrolled in one of the following Levels:
Graduate

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