Geometric Statistics in Machine Learning w/ geomstats with Nina Miolane - TWiML Talk #196
Published:Nov 1, 2018 16:40
•1 min read
•Practical AI
Analysis
This article summarizes a podcast episode featuring Nina Miolane discussing geometric statistics in machine learning. The focus is on applying Riemannian geometry, the study of curved surfaces, to ML problems. The discussion highlights the differences between Riemannian and Euclidean geometry and introduces Geomstats, a Python package designed to simplify computations and statistical analysis on manifolds with geometric structures. The article provides a high-level overview of the topic, suitable for those interested in the intersection of geometry and machine learning.
Key Takeaways
- •The article discusses the application of Riemannian geometry to machine learning.
- •It highlights the differences between Riemannian and Euclidean geometry.
- •It introduces Geomstats, a Python package for simplifying computations on manifolds.
Reference
“In this episode we’re joined by Nina Miolane, researcher and lecturer at Stanford University. Nina and I spoke about her work in the field of geometric statistics in ML, specifically the application of Riemannian geometry, which is the study of curved surfaces, to ML.”