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SUMMARY:Probabilistic machine learning as an algorithmic interface to weat
 her model and environmental data - Charlie Kirkwood | University of Exeter
DTSTART:20210309T110000Z
DTEND:20210309T123000Z
UID:TALK155398@talks.cam.ac.uk
CONTACT:Tudor Suciu
DESCRIPTION:As the volume of data we collect and generate skyrockets\, how
  can we maximise the utility of this data for the purposes of environmenta
 l science and management? In this talk I will outline the challenge of our
  current situation\, and why I think probabilistic machine learning should
  be part of the solution (I get the feeling you won’t be a tough crowd o
 n this point!). I will share examples from my research at the University o
 f Exeter and the Met Office\, including quantile regression forests for we
 ather forecast post-processing\, and Bayesian deep learning for end-to-end
  modelling of environmental variables – atmospheric and lithospheric. Th
 e insights from these projects contribute to the idea of ‘algorithmic in
 terfaces’ as key to the future provision of environmental information.
LOCATION:https://zoom.us/j/6708259482?pwd=Qk03U3hxZWNJZUZpT2pVZnFtU2RRUT09
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