Talk / Overview

Multi-parameter persistent homology is a recent generalization of persistent homology, a technique from algebraic topology that is useful for data analysis. However, the mathematical background required to understand these techniques can be off-putting. In this talk, we'll look at the big ideas and concepts behind these techniques from topological data analysis, without getting into the details of the math. We'll also look at some existing software tools you can use to include these techniques in your data processing repertoire and discuss some techniques for integrating persistent homology with machine learning.

Talk / Speakers

Bryn Keller

Research Scientist, Intel Labs

AMLD / Global partners