Lecture

Data Classification: Gaussian Mixture Models

Related lectures (29)

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

Covers Gaussian Mixture Models, Denoising, Data Classification, and Spike Sorting using Principal Component Analysis.

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

Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.

Covers the Likelihood Ratio Test and hypothesis testing methods using Maximum Likelihood Estimators.

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.

Explores EMG signal analysis, mixture models, Gaussian models, and spike sorting in neural signal processing using PCA.

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

Covers variational inference and neural networks for classification tasks.

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

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.