Data Classification: Gaussian Mixture Models

Related lectures (29)

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Explores parametric spectrum estimation methods, including line and smooth spectra, and delves into heart rate variability analysis.

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

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

Explores modeling neurobiological signals with Markov Chains, focusing on parameter estimation and data classification.

Explores latent variable models, EM algorithm, and Jensen's inequality in statistical modeling.

Explores the likelihood of data under a model and the concept of maximum likelihood.

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

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

Covers parametric signal models and practical Matlab applications for Markov chains and AutoRegressive processes.

Introduces Maximum Likelihood Estimation in econometrics, covering principles, properties, applications, and specification tests.