ECE 256Statistical Signal Processing
Covers fundamental approaches to designing optimal estimators and detectors of deterministic and random parameters and processes in noise, and includes analysis of their performance. Binary hypothesis testing: the Neyman-Pearson Theorem. Receiver operating characteristics. Deterministic versus random signals. Detection with unknown parameters. Optimal estimation of the unknown parameters: least square, maximum likelihood, Bayesian estimation. Will review the fundamental mathematical and statistical techniques employed. Many applications of the techniques are presented throughout the course. Note: While a review of probability and statistics is provided, this is not a basic course on this material. (Formerly EE 262.)
Prerequisite(s): ECE 103 and CSE 107, or permission of instructor.
Find a section and add it to your scheduleLive seat counts, time-conflict checks and the walk from your previous class.
When it runs
Fall 2025Open Fall 2025
| Section | Type | When | Where | Instructor | Seats |
|---|---|---|---|---|---|
| 01 | Lecture | Mon Wed 5:20pm–6:55pm | J Baskin Engr 169 | H. Ye | Open 9/10 |
SlugAtlas is a student project and is not affiliated with, endorsed by, or operated by UC Santa Cruz. Course data is a snapshot of the public class search; myUCSC is authoritative for enrolment.