Learn about probabilistic criteria that define learning through an exploration of maximum a posteriori and maximum likelihood learning principles in this 36-minute lecture from the University of Utah Data Science program. Examine practical examples that demonstrate these Bayesian learning concepts while gaining insights into probabilistic approaches to machine learning. Delve into detailed explanations and demonstrations that help build a strong foundation in probabilistic machine learning frameworks.