Learn about probabilistic criteria for defining machine learning concepts in this university lecture, exploring maximum a posteriori and maximum likelihood learning criteria through detailed examples. Delve into Bayesian learning principles and their practical applications in modern machine learning approaches. Gain insights into probabilistic frameworks that form the foundation of many machine learning algorithms and methodologies.