In this lecture, we will look at the naive Bayes classifier. We will first see how we can predict labels using the maximum a posteriori criterion and then examine the naive Bayes assumption. Then we will see how we can learn the naive Bayes classifier using a probabilistic criterion. Finally, we will look at some practical issues, with specific focus on smoothing.
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