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Probabilistic machine learning: foundations and frontiers

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If you have a question about this talk, please contact Peter Watson .

Probabilistic modelling provides a mathematical framework for understanding what learning is, and has therefore emerged as one of the principal approaches for designing computer algorithms that learn from data acquired through experience. I will review the foundations of the field of probabilistic machine learning. I will then highlight some current areas of research at the frontiers of machine learning, leading up to topics such as Bayesian deep learning, probabilistic programming, Bayesian optimisation, the rational allocation of computational resources, and the Automatic Statistician.

This talk is part of the Cambridge Statistics Discussion Group (CSDG) series.

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