Detailed Course Information

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CS 490CF - Implementing Probabilistic Expert Systems
This course examines the effects on computational efficiency in practice by implementing various techniques for constructing probability distributions in probabilistic expert systems. Topics include Bayesian networks, join tree propagation, and direct computation techniques. Written permission of the Instructor is required to register. 3.000 Credit hours 0.000 TO 3.000 Lecture hours 0.000 TO 3.000 Other hours Levels: Undergraduate Schedule Types: Lecture, Directed Reading, Examination Computer Science Department Restrictions: Must be enrolled in one of the following Levels: Undergraduate Graduate

CS 490CF - Implementing Probabilistic Expert Systems

This course examines the effects on computational efficiency in practice by implementing various techniques for constructing probability distributions in probabilistic expert systems. Topics include Bayesian networks, join tree propagation, and direct computation techniques. ** Written permission of the Instructor is required to register. **
3.000 Credit hours
0.000 TO 3.000 Lecture hours
0.000 TO 3.000 Other hours

Levels: Undergraduate
Schedule Types: Lecture, Directed Reading, Examination

Computer Science Department

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

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Detailed Course Information

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