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SUMMARY:Probabilistic machine learning: foundations and frontiers - Zoubin
  Ghahramani\, Department of Engineering
DTSTART:20171010T181500Z
DTEND:20171010T203000Z
UID:TALK69543@talks.cam.ac.uk
CONTACT:Peter Watson
DESCRIPTION:Probabilistic modelling provides a mathematical framework for 
 understanding what learning is\, and has therefore emerged as one of the p
 rincipal 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 area
 s of research at the frontiers of machine learning\, leading up to topics 
 such as Bayesian deep learning\, probabilistic programming\, Bayesian opti
 misation\, the rational allocation of computational resources\, and the Au
 tomatic Statistician.
LOCATION:Cognition and Brain Sciences Unit\, Chaucer Road\, Cambridge
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