(ML 16.7) EM for the Gaussian mixture model (part 1)
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 16.8) EM for the Gaussian mixture model (part 2)
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(ML 16.4) Why EM makes sense (part 1)
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Clustering (4): Gaussian Mixture Models and EM
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The Exponential Family (Part 1)
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EM Algorithm : Data Science Concepts
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(ML 16.5) Why EM makes sense (part 2)
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Expectation Maximization: how it works
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Hidden Markov Models 12: the Baum-Welch algorithm
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Support Vector Machines Part 1 (of 3): Main Ideas!!!
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The Big Short (2015): The Jenga Scene – Explaining the Financial Collapse
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Dangerous Grindstone Installation in 1971
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Gaussian Mixture Models
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Multivariate Normal (Gaussian) Distribution Explained
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Probabilistic ML — Lecture 22 — Mixture Models
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(ML 16.6) Gaussian mixture model (Mixture of Gaussians)
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27. EM Algorithm for Latent Variable Models
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From Child Prodigy to Winning Fields Medal, Nobel of Math
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We're 99.9% sure this pattern is true, but no one can prove it
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I finally understood why the universe needs imaginary numbers (My mind is blown!)
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