EC ENGR 210A · EE 210AAdaptation and Learning
Electrical and Computer Engineering · 4 units · Graduate courses (200-299)
Mean-square-error estimation and filters, least-squares estimation and filters, steepest-descent algorithms, stochastic-gradient algorithms, convergence, stability, tracking, and performance, algorithms for adaptation and learning, adaptive filters, learning and classification, optimization.
Letter grading.
When it runs
Requisites
Official UCLA wording
Preparation: prior training in probability theory, random processes, and linear algebra. Recommended requisites: courses 205A, 241A.
BruinTree reads · Preparation
needs reviewconfidence 0.00 · from textBruinTree could not read this requirement — see UCLA’s wording above.
- · could not read "prior training in probability theory" (no course number found)
- · could not read "random processes" (no course number found)
- · could not read "linear algebra" (no course number found)
- · no course reference could be read from this requisite
BruinTree reads · Recommended
confidence 1.00 · from textRequires
Everything that has to come before this course, not just the courses named in the requisite above.
EC ENGR 210A
- EC ENGR 205AMatrix Analysis for Scientists and Engineers
- EC ENGR 241AStochastic Processes
- EC ENGR 131AProbability and Statistics
2 direct requisites. Showing 5 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.
EC ENGR 210A
- EC ENGR 210BInference over Networks
1 course lists this as a requisite. The whole downstream is here — 1 course over 1 level. Every course here opens its own tree.