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 2025 - Homework 4 was due after the midterm, but the material on that homework was tested on the midterm. Having the solutions would have been super helpful. We also didn't have a Campuswire or Piazza for this class, which could've been helpful given how difficult some of the homeworks were. I wish the slides had more examples, and sometimes the slides and textbook would have inconsistent notation. The midterm was quite challenging as a good portion of questions on the midterm tested topics not included in the homework.
Winter 2025 - Homework 4 was due after the midterm, but the material on that homework was tested on the midterm. Having the solutions would have been super helpful. We also didn't have a Campuswire or Piazza for this class, which could've been helpful given how difficult some of the homeworks were. I wish the slides had more examples, and sometimes the slides and textbook would have inconsistent notation. The midterm was quite challenging as a good portion of questions on the midterm tested topics not included in the homework.
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