Professor Zoubin Ghahramani gives a talk on probabilistic modelling from it's foundations to current areas of research at the frontiers of machine learning.
Strachey Lecture - Probabilistic machine learning: foundations and frontiers
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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. Professor Ghahramani will review the foundations of this field, from basics to Bayesian nonparametric models and scalable inference. He will then highlight some current areas of research at the frontiers of machine learning, leading up to topics such as probabilistic programming, Bayesian optimisation, the rational allocation of computational resources, and the Automatic Statistician.
The Strachey lectures are generously supported by OxFORD Asset Management.
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This series is host to episodes created by the Department of Computer Science, University of Oxford, one of the longest-established Computer Science departments in the country.
The series reflects this department's world-class research and teaching by providing talks that encompass topics such as computational biology, quantum computing, computational linguistics, information systems, software verification, and software engineering.