(= A PHY 451/451Y & I CSI 451) Bayesian Data Analysis and Signal Processing

Catalog description

This course will introduce both the principles and practice of Bayesian and maximum entropy methods for data analysis, signal processing, and machine learning. This is a hands-on course that will introduce the use of the MATLAB computing language for software development. Students will learn to write their own Bayesian computer programs to solve problems relevant to physics, chemistry, biology, earth science, and signal processing, as well as hypothesis testing and error analysis. Optimization techniques to be covered include gradient ascent, fixed-point methods, and Markov chain Monte Carlo sampling techniques. Only one version may be taken for credit. Prerequisite(s): A MAT 214 (or equivalent) and I CSI/I ECE 201.