Classification with GMM

This lecture covers the transition from clustering to classification using Gaussian Mixture Models (GMM). It explains binary classification, determining boundaries between clusters, parameter estimation, Gaussian Discriminant Rule, optimal Bayes classifier, and classification with two Gaussians. The lecture also delves into Maximum Likelihood Discriminant Rule for both single-class and multi-class problems, showcasing examples of 4-classes classification. It concludes with the challenges of unbalanced datasets in GMM-based classification.