227-0427-10L Advanced Signal Analysis, Modeling, and Machine Learning
Semester | Frühjahrssemester 2021 |
Dozierende | H.‑A. Loeliger |
Periodizität | jährlich wiederkehrende Veranstaltung |
Lehrsprache | Englisch |
Kurzbeschreibung | The course develops a selection of topics pivoting around graphical models (factor graphs), state space methods, sparsity, and pertinent algorithms. |
Lernziel | 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 |
Skript | Lecture notes |
Voraussetzungen / Besonderes | Solid mathematical foundations (especially in probability, estimation, and linear algebra) as provided by the course "Introduction to Estimation and Machine Learning". |