Data Classification: Gaussian Mixture Models

Related lectures (29)

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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.