This course teaches learners how to apply Maximum Likelihood estimation in Bayesian Networks. The course covers topics such as where parameters come from, learning tasks, parameter sharing, Naive Bayes, HMMs, Laplace smoothing, Expectation Maximization, and more. The teaching method includes theoretical explanations, examples, and scenarios. This course is intended for individuals interested in advancing their knowledge of Bayesian Networks and AI.
Bayesian Networks 3 - Maximum Likelihood - Stanford CS221: AI (Autumn 2019)
Stanford University via YouTube
Overview
Syllabus
Introduction.
Announcements.
Review: Bayesian network.
Review: probabilistic inference.
Where do parameters come from?.
Roadmap.
Learning task.
Example: one variable.
Example: v-structure.
Example: inverted-v structure.
Parameter sharing.
Example: Naive Bayes.
Example: HMMS.
General case: learning algorithm.
Maximum likelihood.
Scenario 2.
Regularization: Laplace smoothing.
Example: two variables.
Motivation.
Maximum marginal likelihood.
Expectation Maximization (EM).
Taught by
Stanford Online
