STATS 238

Vision as Bayesian Inference

Statistics · 4 units · Graduate courses (200-299)

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Formulation of vision as Bayesian inference using models developed for designing artificial vision systems. Applied to statistics, they define ideal observer models that can be used to model human performance and serve a benchmark.

S/U or letter grading.

Requisites

Official UCLA wording
Requisite: course 100A or 200A.

BruinTree reads · Prerequisite

confidence 1.00 · from text

one of

Requires

Everything that has to come before this course, not just the courses named in the requisite above.

STATS 238

STATS 100AIntroduction to Probability
- MATH 32BCalculus of Several Variables
  - MATH 31BIntegration and Infinite Series1 more beneath
  - MATH 32ACalculus of Several Variables1 more beneath
- MATH 33ALinear Algebra and Applications
  - MATH 3BCalculus for Life Sciences Students1 more beneath
  - MATH 31B
STATS 200AApplied Probability
- STATS 100AIntroduction to Probabilityanother path to it
- MATH 170AProbability Theory I
  - MATH 32BCalculus of Several Variablesanother path to it
  - MATH 33ALinear Algebra and Applicationsanother path to it

2 direct requisites. Showing 14 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.

No course in the catalog lists this as a requisite.