COM SCI 262A
Learning and Reasoning with Bayesian Networks
Computer Science · 4 units · Graduate courses (200-299)
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.
Requisites
Official UCLA wording
Requisite: course 112 or Electrical Engineering 131A.
BruinTree reads · Prerequisite
confidence 1.00 · from textRequires
Everything that has to come before this course, not just the courses named in the requisite above.
COM SCI 262A
- COM SCI 112Modeling Uncertainty in Information Systems
- COM SCI 111Operating Systems Principles
- COM SCI 33Introduction to Computer Organization
- C&EE 110Introduction to Probability and Statistics for Engineers
- EC ENGR 131AProbability and Statistics
2 direct requisites. Showing 22 courses over 3 levels; the branches marked with a count carry on past it. Every course here opens its own tree.
Unlocks
What this course is a requisite for, and what those courses lead to in turn.
COM SCI 262A
- COM SCI 262ZCurrent Topics in Cognitive Systems
- STATS M241Current Topics in Causal Modeling, Inference, and Reasoning
2 courses list this as a requisite. The whole downstream is here — 2 courses over 1 level. Every course here opens its own tree.