Computational learning in dynamical systems

The aim of this course is to provide an introduction to the theory and application of computational methods for inference in dynamical systems. More specifically, the computational methods we are referring to are sequential Monte Carlo (SMC) methods (particle filters and particle smoothers) for nonlinear state inference problems and expectation maximisation (EM) and Markov chain Monte Carlo (MCMC) methods for nonlinear system identification.

Links to complete course information

This course is currently under development and as part of this development process I offer the course at various universities around the world. Links to the various editions are available here:

  • UTFSM, Valparaíso, Chile, January 2014, home page.
  • KTH, Stockholm, Sweden, November 2012, home page.
  • USYD, Sydney, Australia, October 2012, home page.
  • VUB, Brussels, Belgium, June 2012, home page.

 © Thomas Schön 2020