With Peter Damaschke. This year I am extending the course to include some modern developments in combinatorial bandit optimization
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With Peter Ljunlof. This year I am modernising the course with the inclusion of probabilistic inference and planning and reinforcement learning; a high-level overview of some of the topics in the decision theory course
A general overview of hypothesis testing is given. The Bayesian and distribution-free framework to multiple hypothesis testing and to null hypothesis testing are discussed. Some practical algorithms are introduced, together with associated performance bounds.
This tutorial examines simple physical models of vehicle dynamics and overviews methods for parameter estimation and control. Firstly, techniques for the estimation of parameters that deal with constraints are detailed. Secondly, methods for controlling the system are explained. Thirdly, we discuss trajectory optimization.