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An introduction to clustering and the expectation maximisation algorithm Part 1

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Please note, this event will be recorded. Microsoft will own the copyright of any recording and reserves the right to distribute it as required.

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.

This talk is part of the Microsoft Research Cambridge, public talks series.

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