This repo aims to understand the 2020 paper by McInnes, Healy and Melville called UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction in order to use the UMAP package for various statistical applications. The paper is heavy in category theory, algebraic topology and differential geometry. Therefore the Guide to UMAP will break down the various concepts and definitions encountered in the paper so that any mathematics or statistics graduate student should (with some effort) be able to understand it. The idea is that this guide is to accompany the reading of th original paper to help you work through mathematical background and gain further intuition where needed.

After this is completed, I will work through some applications of UMAP with some interesting datasets to be determined.