COM SCI 112
Modeling Uncertainty in Information Systems
Computer Science · 4 units · Undergraduate upper division (100-199)
Designed for juniors/seniors. Probability and stochastic process models as applied in computer science. Basic methodological tools include random variables, conditional probability, expectation and higher moments, Bayes theorem, Markov chains. Applications include probabilistic algorithms, evidential reasoning, analysis of algorithms and data structures, reliability, communication protocol and queueing models.
Letter grading.
Requisites
Official UCLA wording
Enforced requisites: course 111 and one course from Civil Engineering 110, Electrical Engineering 131A, Mathematics 170A, or Statistics 100A.
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 112
- COM SCI 111Operating Systems Principles
- COM SCI 32Introduction to Computer Science II
- COM SCI 31Introduction to Computer Science I
- COM SCI 33Introduction to Computer Organization
Unlocks
What this course is a requisite for, and what those courses lead to in turn.
COM SCI 112
- COM SCI 212AQueueing Systems Theory
- COM SCI 218Advanced Computer Networks
- COM SCI 246Web Information Management
- COM SCI 262ALearning and Reasoning with Bayesian Networks
- COM SCI 262ZCurrent Topics in Cognitive Systems