naive Bayes

(55 minutes to learn)


Naive Bayes is a modeling assumption used in classification, where we assume the observed data are conditionally independent given their class assignments. Despite its name, the standard naive Bayes model does not use Bayesian inference, but rather, a maximum likelihood estimation.


This concept has the prerequisites:

Core resources (read/watch one of the following)


Coursera: Machine Learning
An online machine learning course aimed at advanced undergraduates.
Author: Pedro Domingos
Other notes:
  • Click on "Preview" to see the videos.
Stanford's Machine Learning lecture notes
Lecture notes for Stanford's machine learning course, aimed at graduate and advanced undergraduate students.
Author: Andrew Y. Ng
The Elements of Statistical Learning
A graudate-level statistical learning textbook with a focus on frequentist methods.
Authors: Trevor Hastie,Robert Tibshirani,Jerome Friedman
Bayesian Reasoning and Machine Learning
A textbook for a graudate machine learning course.
Author: David Barber

Supplemental resources (the following are optional, but you may find them useful)



See also