227-0427-10L Advanced Signal Analysis, Modeling, and Machine Learning

Abstract	The course develops a selection of topics pivoting around graphical models (factor graphs), state space methods, sparsity, and pertinent algorithms.
Learning objective	The course develops a selection of topics pivoting around factor graphs, state space methods, and pertinent algorithms: - factor graphs and message passing algorithms - hidden-Markov models - linear state space models, Kalman filtering, and recursive least squares - Gaussian message passing - Gibbs sampling, particle filter - recursive local polynomial fitting & applications - parameter learning by expectation maximization - sparsity and spikes - binary control and digital-to-analog conversion - duality and factor graph transforms
Lecture notes	Lecture notes
Prerequisites / Notice	Solid mathematical foundations (especially in probability, estimation, and linear algebra) as provided by the course "Introduction to Estimation and Machine Learning".