In this lecture, we will see the popular naive Bayes classifier and its decision boundary. We will work through examples that illustrate maximum likelihood learning with the naive Bayes assumption. Then we will see a few practical aspects to keep in mind when training a model with the assumption.
This will take us on a discussion about generative and discriminative models.
Links and Resources
Chapter 6 of Tom Mitchell’s book
Chapter 7 of A Course in Machine Learning by Hal Daume
Chapters 2 and 3 of Pattern Classification by Duda, Hart and Stork