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Expectation maximization algorithm / KTU Machine Learning

#emalgorithm #expectationmaximization #machinelearning Maximum likelihood becomes intractable if there are variables that interact with those in the dataset but were hidden or not observed, so-called latent variables. The expectation-maximization algorithm is an approach for performing maximum likelihood estimation in the presence of latent variables. It does this by first estimating the values for the latent variables, then optimizing the model, then repeating these two steps until convergence. It is an effective and general approach and is most commonly used for density estimation with missing data, such as clustering algorithms like the Gaussian Mixture Model. This lecture covers the topics clustering as a gaussian mixture model and the outline of EM algorithm. Notes https://drive.google.com/file/d/1EV2w...