EE262: Statistical Signal Processing

*****COURSES ARE SUBJECT TO CHANGE*****

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. Prerequisite(s): course 103 and Computer Engineering 107, or permission of instructor. P. Milanfar

5 Credits

YearFallWinterSpringSummer
2017-18
2016-17
2015-16
2014-15
2013-14
  • Section 01
    Alyson K Fletcher (akfletch)
    Will be telecast to SV
  • Section 50
    Alyson K Fletcher (akfletch)
    SV telecast section
2012-13
  • Section 01
    Alyson K Fletcher (akfletch)
    Telecast to SV
  • Section 50
    Alyson K Fletcher (akfletch)
    SV Telecast
2011-12
2010-11
  • Section 01
    Peyman Milanfar (milanfar)
    Telecast to SVC
2009-10
2008-09
2007-08
2006-07
2005-06
2004-05
2003-04

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