An introduction to clustering and the expectation maximisation algorithm Part 1 – Richard Turner Microsoft Research Ltd

When:
April 10, 2019 @ 10:30 am – 12:00 pm
2019-04-10T10:30:00+01:00
2019-04-10T12:00:00+01:00
Where:
Auditorium
Microsoft Research Ltd, 21 Station Road, Cambridge
CB1 2FB
Contact:
Microsoft Research Cambridge Talks Admins

Clustering methods assign ‘similar’ data points to the same cluster, and ‘dissimilar’ data points to different clusters. They find application in a diverse range of application areas including data-driven 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.

In the first lecture I will start by giving an introduction to one of the simplest clustering 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.