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SUMMARY:An introduction to clustering and the expectation maximisation alg
 orithm Part 2 - Richard Turner Microsoft Research Ltd
DTSTART:20190411T083000Z
DTEND:20190411T100000Z
UID:TALK122686@talks.cam.ac.uk
CONTACT:Microsoft Research Cambridge Talks Admins
DESCRIPTION:Clustering methods assign ‘similar’ data points to the sam
 e cluster\, and ‘dissimilar’ data points to different clusters. They f
 ind application in a diverse range of application areas including data-dri
 ven understanding of disease sub-types\, identification of communities in 
 social networks\, and email spam filtering. Clustering is therefore one of
  the central tasks in unsupervised machine learning.\n\nIn the second lect
 ure I will describe how learning in this model will be handled through the
  Expectation Maximisation (EM) algorithm which can be deployed to many lat
 ent variable models. We will cover this from the general variational view 
 point connecting to the wider class of variational inference methods. Fina
 lly we will look at the behaviour of the mixture of Gaussians EM algorithm
  and identify strengths and weaknesses of the approach.\n
LOCATION:Auditorium\, Microsoft Research Ltd\, 21 Station Road\, Cambridge
 \, CB1 2FB
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