| Type: | Ph.D.-level, Open University
Language: English |
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Recommended semester: 7th - 9th semester
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Scope and form: Lectures and exercises
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Examination: Evaluation of report(s) (Pass/fail)
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| Contact person: | Carl edward Rasmussen |
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Department: Informatics and Mathematical Modelling
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Aim: The aim of this course is to discuss various topics that are relevant to non-linear modelling in situations where no parametric model of the system is available. We will address fundamental issues involved, and describe methods including both Bayesian and non-Bayesian approaches. The choice of topics is not exhaustive but rather governed by our personal experiences and inclinations. Full description including schedule is available.
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Contents: Probabilistic model fitting, Generalisation estimation, Regularisation, Bayesian learning, Markov Chain methods, Flexible metric kernel methods, Neural networks, variational methods, Gaussian Processes, Infinite Mixtures.
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