Gaussian Mixture Models: Data Classification

This lecture covers the concept of denoising discrete valued signals using Gaussian mixture models. It explains how to model samples generated by different classes and the process of data classification. The instructor demonstrates the application of the EM algorithm for maximizing the likelihood function of Gaussian mixture models. The lecture also delves into the analysis of EMG signals for the classification of muscular pathologies and the segmentation of images using Markovian Gaussian mixture models.