EC ENGR 210A
Adaptation 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
Checking the Schedule of Classes…
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.