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.

  • Fall 2013

    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.
  • Spring 2013,

    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.