Explains how heirarchical clustering finds smaller subgroups within larger differentiated clusters, and how these groups can be visualized on a dendogram.

Explains how unsupervised machine learning's purpose is to create new groups to put data into, and the clustering types used in this process.

Explains how the silhouette method evaluates unsupervised machine learning processes by measuring the closeness of data points.

Illustrates how once-separate diagnoses were placed together under the diagnosis of autism spectrum disorder thanks to heirarchical clustering, resulting in better treatment regimens.

Defines and gives some brief examples of neural networks.