Probabilistic Models

29 Novembro 2016, 13:00 • João Marques Silva

This week's lecture focused on probabilistic models. The first part of the lecture related uncertainty with probability, and with decision theory. This part of the lecture also covered basic definitions of probability, random variables, events, prior and posterior probabilities, independence and conditional independence, inference by enumeration, and Bayes' theorem. The second part of the lecture provided as overview of statistical learning, including MAP and ML learning, the Bayesian optimal classifier, and the Naive Bayes classifier. The lecture also analyzed a number of examples.