ISTA311
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ISTA311 - Foundation of Information and Inference
Course Description
An introduction to the mathematical theories of probability and information as tools for inference, decision-making, and efficient communication. Topics include discrete and continuous random variables, measures of information and uncertainty, discrete time/discrete state Markov chains, elements of Bayesian inference and decision-making, Bayesian and Maximum Likelihood parameter estimation, and elementary coding theory.
Min Units
3
Max Units
3
Repeatable for Credit
No
Grading Basis
GRD - Regular Grades A, B, C, D, E
Career
Undergraduate
Enrollment Requirements
017253
May be convened with
Component
Lecture
Optional Component
No
Typically Offered Main Campus
Fall, Spring