Data Classification: Mixture Models

This lecture covers the analysis of EMG signals for the classification of muscular pathologies using data classification techniques. It delves into mixture models with i.i.d. and Markovian classes, Gaussian mixture models, and the EM algorithm for maximizing the likelihood function. The lecture also discusses spike sorting in neural signal processing, data sets characterization, and principal component analysis (PCA) for reducing the number of variables.