Mathematical analysis of decision making. Bayesian inference and risk. Maximum likelihood and nonparametric methods. Algorithmic methods for decision rules: perceptrons, neural nets, and back propagation. Hidden Markov models, Blum-Welch, principal and independent components.
ES 155 is the first course on Biological Signal Processing, the science of collection, representation, manipulation, transformation, storage of biological signals and the use of modern scientific computing tools to interpret biological signals and tell engaging and informative stories using biological data. The spirit of the class and example student projects can be found below