Mathematics for Signal Processing and Machine Learning. (3-0) 3 Credit Hours

Catalog description

Prerequisite: Graduate standing or consent of instructor. This course covers fundamental and advanced linear algebra concepts such as vectors, matrices, factorizations, norms, and least squares. It delves into probability theory and random processes, including joint/conditional probabilities, Bayes' theorem, multivariate distributions, and moments. In the domain of multivariate calculus, gradients and Hessians are explored. Basic optimization topics include convex optimization, KKT conditions, and elements of stochastic optimization. Additional subjects include complex analysis and signal/systems theory, encompassing sampling theory, convolution, filtering, LTI systems, interpolation, and Fourier transform. This course has Differential Tuition.