Lecture 24 – Clustering, Part 1
Presented by Isaac Schmidt
Content by Josh Hug
A reminder – the right column of the table below contains Quick Checks. These are not required but suggested to help you check your understanding.
Introduction to clustering. Examples of clustering in practice.
The K-Means clustering algorithm. Example of K-Means clustering.
Loss functions for K-Means. Inertia and distortion. Optimizing inertia.
Agglomerative clustering as an alternative to K-Means. Example of agglomerative clustering. Dendrograms and other clustering algorithms.
Picking the number of clusters. The elbow method and silhouette scores. Summary of clustering and machine learning.