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An introduction to clustering and the expectation maximisation algorithm Part 1

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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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