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The course covers the theory of reproducing kernel Hilbert spaces and some of their applications to various engineering fields. It will provide basic concepts of Hilbert spaces, followed by the definition of a reproducing kernel (scalar-, vector- or operator-valued) and their fundamental properties. Applications will include problems in approximation theory, machine learning and statistical modeling (Gaussian processes). Prerequisites: Linear algebra, Real Analysis, Probability and Statistics.