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SUMMARY:An introduction to clustering and the expectation maximisation alg
 orithm Part 1 - Richard Turner Microsoft Research Ltd
DTSTART:20190410T093000Z
DTEND:20190410T110000Z
UID:TALK122683@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 first lectu
 re I will start by giving an introduction to one of the simplest clusterin
 g techniques\, the k-means algorithm. We will then discuss its limitations
  and motivate a probabilistic approach to clustering using the mixture of 
 Gaussians model and maximum likelihood learning.\n
LOCATION:Auditorium\, Microsoft Research Ltd\, 21 Station Road\, Cambridge
 \, CB1 2FB
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