  This tutorial paper provides an introduction to recently developed tools for
machine learning, especially learning dynamical systems (system
identification), with stability and robustness constraints. The main ideas are
drawn from contraction analysis and robust control, but adapted to problems in
which large-scale models can be learnt with behavioural guarantees. We
illustrate the methods with applications in robust image recognition and system
identification.
