COM SCI 262A
Learning and Reasoning with Bayesian Networks
Description: Lecture, four hours; outside study, eight hours. Requisite: course 112 or Electrical Engineering 131A. Review of several formalisms for representing and managing uncertainty in reasoning systems; presentation of comprehensive description of Bayesian inference using belief networks representation. Letter grading.
Units: 4.0
Units: 4.0
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Most Helpful Review
Winter 2022 - Great professor. All of the past lecture videos can be found on Youtube as well. The tests were completely fair; I just slightly underperformed the test averages. I think Bayesian Networks are also pretty interesting and fun to learn about, so do take this class if you get the opportunity! Also the TA, Scott Mueller, was super helpful.
Winter 2022 - Great professor. All of the past lecture videos can be found on Youtube as well. The tests were completely fair; I just slightly underperformed the test averages. I think Bayesian Networks are also pretty interesting and fun to learn about, so do take this class if you get the opportunity! Also the TA, Scott Mueller, was super helpful.
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