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

SemesterSpring Semester 2021
LecturersH.‑A. Loeliger
Periodicityyearly recurring course
Language of instructionEnglish

AbstractThe course develops a selection of topics pivoting around graphical models (factor graphs), state space methods, sparsity, and pertinent algorithms.
ObjectiveThe 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 notesLecture notes
Prerequisites / NoticeSolid mathematical foundations (especially in probability, estimation, and linear algebra) as provided by the course "Introduction to Estimation and Machine Learning".