UMAP is one of the most popular dimension-reductions algorithms and this StatQuest walks you through UMAP, one step at a time, so that you will have a solid understanding of how UMAP works.

NOTE: This StatQuest is based on the original UMAP manuscript...
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...specifically Appendix C, From t-SNE to UMAP, which is also here...
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...and the UMAP user documentation...
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0:00 Awesome song and introduction
1:07 Motivation for UMAP
2:55 UMAP main ideas
5:22 Calculating high-dimensional similarity scores
10:41 Getting started with the low-dimensional graph
12:37 Calculating low-dimensional similarity scores and moving points
15:49 UMAP vs t-SNE

#StatQuest #UMAP #DimensionReduction

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