# 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.

Video | Quick Check | |
---|---|---|

24.0 Introduction. |
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24.1 Introduction to clustering. Examples of clustering in practice. |
24.1 | |

24.2 The K-Means clustering algorithm. Example of K-Means clustering. |
24.2 | |

24.3 Loss functions for K-Means. Inertia and distortion. Optimizing inertia. |
24.3 | |

24.4 Agglomerative clustering as an alternative to K-Means. Example of agglomerative clustering. Dendrograms and other clustering algorithms. |
24.4 | |

24.5 Picking the number of clusters. The elbow method and silhouette scores. Summary of clustering and machine learning. |
24.5 |