Advanced Topics in Signal Processing and Machine Learning. (3-0) 3 Credit Hours

Catalog description

Prerequisite: EE 5153 or EE 5163 , or consent of instructor. Topics may include the following. Topic 1: Nonlinear Filters. Includes order statistic filters, morphological filters, stack/Boolean filters, and other related topics. Topic 2: Detection and Estimation Theory. Includes minimum-variance unbiased estimation, Cramer-Rao low bound, maximum-likelihood estimation, Bayesian estimation, Neyman-Pearson detector, Bayesian detector, matched filter, and Generalized Likelihood Ratio Test. Topic 3: Orthogonal and Wavelet Transforms with Applications. Includes a broad spectrum of orthogonal transforms, including Cosine, Sine, Hartley, Haar, Slant, Short-time Fourier, Gabor, and Walsh, along with wavelet and sub-band decompositions. Covers the construction, properties, and multiresolution analysis of wavelets and wavelet packets and applications in image and video compression standards, signal and image denoising, steganography, and watermarking. Topic 4: Signal Processing for Wireless Systems. Converse use of transforms for the analysis and design of wireless systems, filter design, and/or adaptive antenna-array processing. Topic 5: Information Theory. Covers concepts like entropy, information content, channel capacity, and the fundamental limits of signal processing and communication systems. Topic 6: Game Theory. Covers mathematical modeling of conflict and cooperation between rational decision-makers, classic games analysis, Nash equilibrium, and the impact of asymmetric information. Includes application examples to real-world scenarios such as wireless communications and AI agents. May be repeated for credit as topics vary. Generally Scheduled Location: Downtown Campus, Main Campus, Internet. This course has Differential Tuition.