Expositor: José A. Perea
Procedencia: Michigan State University
Abstract: Many problems in modern data science can be phrased as topological questions: e.g., clustering is akin to finding the connected components of a space, and regression/classification can be thought of as learning maps between structured spaces. I will describe in this talk how tools from algebraic topology can be leveraged for the analysis of complex data sets. Several illustrative examples will be provided, including applications to computer vision, machine learning and computational biology.