(ML 16.8) EM for the Gaussian mixture model (part 2)
Applying EM (Expectation-Maximization) to estimate the parameters of a Gaussian mixture model. Here we use the alternate formulation presented for (unconstrained) exponential families.
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(ML 16.9) EM for the Gaussian mixture model (part 3)
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(ML 16.6) Gaussian mixture model (Mixture of Gaussians)
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(ML 19.11) GP regression - model and inference
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Stanford CS229: Machine Learning | Summer 2019 | Lecture 16 - K-means, GMM, and EM
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