EPISODE · Nov 1, 2018 · 43 MIN
Geometric Statistics in Machine Learning w/ geomstats with Nina Miolane - TWiML Talk #196
from The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) · host Sam Charrington
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. In our discussion we review the differences between Riemannian and Euclidean geometry in theory and her new Geomstats project, which is a python package that simplifies computations and statistics on manifolds with geometric structures.
What this episode covers
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. In our discussion we review the differences between Riemannian and Euclidean geometry in theory and her new Geomstats project, which is a python package that simplifies computations and statistics on manifolds with geometric structures.
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Geometric Statistics in Machine Learning w/ geomstats with Nina Miolane - TWiML Talk #196
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