Defines supervised machine learning and describes some of the uses in which it is better for modeling than other statstical models.

Explains why the type of error matters in statistics.

Explains the qualities of a uniform distribution.

Explains how histograms are used, and gives an example.

Explains the definition, use, and some real world examples of deep learning. It also explains their recurrent nature. It also illustrates the difference between feed forward and recurrent neural networks.