Explains how to train machine learning algorithms with training and testing data sets, and the confusion matrix method of analysis.

Illustrates how Bayesian analyses are made in science with null hypotheses.

Explains how splitting data into a classifier and training and test sets can help determine K (the number of similiar data points to be considered).

Explains the K-Nearest Neighbors method of machine learning using the heights of dog breeds. This method assumes that data points that are close together are similiar.

Explains how Bayesian Search Theory was used to locate military submarines.