The document describes the Naive Bayes classifier. It begins with background on probabilistic classification and generative models. It then covers probability basics and the probabilistic classification rule. It introduces Naive Bayes, making the independence assumption between attributes. The Naive Bayes algorithm is described for both the learning and testing phases. An example of classifying whether to play tennis is provided. Issues with Naive Bayes like violating independence and zero probabilities are discussed. Continuous attributes modeled with normal distributions are also covered. It concludes that Naive Bayes training is easy and fast while testing is straightforward, and it often performs competitively despite its independence assumption.