STATS M232A
Statistical Modeling and Learning in Vision and Cognition
Statistics · 4 units · Graduate courses (200-299)
(Same as Computer Science M266A.) Computer vision and pattern recognition. Study of four types of statistical models for modeling visual patterns: descriptive, causal Markov, generative (hidden Markov), and discriminative. Comparison of principles and algorithms for these models; presentation of unifying picture. Introduction of minimax entropy and EM-type and stochastic algorithms for learning.
S/U or letter grading.
Requisites
Official UCLA wording
Preparation: basic statistics, linear algebra (matrix analysis), computer vision.
BruinTree reads · Preparation
needs reviewconfidence 0.00 · from textBruinTree could not read this requirement — see UCLA’s wording above.
- · could not read "basic statistics" (no course number found)
- · could not read "linear algebra (matrix analysis)" (no course number found)
- · could not read "computer vision" (no course number found)
- · no course reference could be read from this requisite
Requires
Everything that has to come before this course, not just the courses named in the requisite above.
Nothing — this is an entry point.
Unlocks
What this course is a requisite for, and what those courses lead to in turn.
STATS M232A
- STATS M232CCognitive Artificial Intelligence
- COMM M232CCognitive Artificial Intelligence
2 courses list this as a requisite. The whole downstream is here — 2 courses over 1 level. Every course here opens its own tree.