K-means and Gaussian Mixture Model

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Description

This lecture covers the K-means algorithm for clustering data into roundish clusters, the properties of K-means, the EM algorithm for the Gaussian mixture model, Jensen's inequality, the Kullback-Leibler divergence, and the Expectation-Maximization algorithm.

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