Demonstrate proficiency in the following areas:

LO 7.1.1 Evidence and probabilities (Ch 9)

For example:

A. Define independent events.

B. Calculate the joint probability of two events.

C. Recognize and apply joint probability using conditional probability.

D. Calculate joint probability for independent and dependent events.

E. Explain Bayes’ Rule with the help of an example.

F. Define posterior probability, prior, likelihood, and conditional independence.

G. Explain the naïve Bayes classifier.

H. Explain why we do not need to calculate the denominator of Bayes’ rule for the

naïve Bayes classifier.

I. List the advantages and disadvantages of the naïve Bayes classifier.

J. Define generative model, lift, and Naïve-Naïve Bayes.

Reading 7.1: Provost, F. and T. Fawcett. (2013). Data Science for Business.

Sebastopol, CA: O’Reilly Media Inc. Chapters 9 & 10.

Book URL: https://www.oreilly.com/library/view/data-science-for/9781449374273/

Errata page URL: https://www.oreilly.com/catalog/errata.csp?isbn=0636920028918