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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).
Stanford Online
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