Lecture 23 – Clustering
Presented by Anthony D. Joseph
Content by Josh Hug
The Quick Check for this lecture is due Monday, November 30th at 11:59PM. A random one of the following Google Forms will give you an alphanumeric code once you submit; you should take this code and enter it into the “Lecture 23” question in the “Quick Check Codes” assignment on Gradescope to get credit for submitting this Quick Check.
| Video | Quick Check | |
|---|---|---|
| 23.1 Introduction to clustering. Examples of clustering in practice. |
23.1 | |
| 23.2 The K-Means clustering algorithm. Example of K-Means clustering. |
23.2 | |
| 23.3 Loss functions for K-Means. Inertia and distortion. Optimizing inertia. |
23.3 | |
| 23.4 Agglomerative clustering as an alternative to K-Means. Example of agglomerative clustering. Dendrograms and other clustering algorithms. |
23.4 | |
| 23.5 Picking the number of clusters. The elbow method and silhouette scores. Summary of clustering and machine learning. |
23.5 |