Gaussian Mixture Model: EM final form

Related lectures (29)

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Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.

Introduces K-means clustering, the Gaussian mixture model, Jensen's inequality, and the EM algorithm.

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

Introduces the Boltzmann Machine, covering expectation consistency, data clustering, and probability distribution functions.

Covers the derivation of the EM algorithm for the Gaussian Mixture Model.

Covers the Expectation Maximization algorithm for learning parameters and dealing with unknown variables.

Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.

Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.