This lecture overviews Bayesian Learning that has many applications in pattern recognition and clustering. It covers the following topics in detail: Bayes probability theorem. Bayes decision rule. Bayesian classification. Maximum A-Posteriori Criterion. Maximum Likelihood Criterion. Normally Distributed Sample Classification. Bayesian clustering.

Linear decision boundary for two 2D Gaussian pdfs having equal C.