Gaussian Mixture Model: EM final form

In course

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Description

This lecture covers the calculations for the E-step and the final results for the M-step in the context of the Gaussian Mixture Model. The instructor explains the expected complete likelihood, the probability distributions, and the updates for the parameters. The pseudocode of the EM algorithm is also presented, providing a summary of the main updates. By the end of the lecture, viewers will have a clear understanding of the algorithm and its application in clustering.

Official source

https://mediaspace.epfl.ch/media/0_4js7rdfl

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Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics, Statistical hypothesis testing

Information engineering

Machine learning: Unsupervised learning

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