Optimization Methods in Signal Processing and Machine Learning

3 credits · College of Engineering catalogue

Advisory Prerequisite: ECE 505 or ECE 551. Enforced Prerequisite: None. (3 credits) Theory and application of optimization methods for signal and image processing and machine learning problems. Algorithms include gradient-based methods, proximal methods, and duality-based methods. Applications include signal denoising, compressed sensing, matrix completion, robust regression, and classifier design.