Winter 2013, 2014 and 2015
UCSC EE262: Detection and Estimation
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. The course includes review of the fundamental mathematical and statistical techniques employed. Many applications of the techniques are presented throughout the course.
UCSC CE/EE293: Sparsity, Dimensionality Reduction, and Machine Learning
Special topics class in sparsity and low-rank methods in approximation, inverse problems and machine
learning. Topics included sparse inverse problems, convex relaxations, random matrix theory,
optimization methods, dictionary learning and applications in image processing and learning.
UCSC CE/EE153: Digital Signal Processing
UCSC CE/EE103: Signals and Systems
Introduction to signals and analog and digital signal processing, a topic that forms an integral part of
systems in many diverse areas, including seismic data processing, communications, speech processing,
image processing, neuroscience, and electronics. Signal and system representations, the Fourier transform, filter and the Laplace transform are all covered.