Title: Algebra & Geometry in Data Science & AI

Abstract:

Analyzing big and structured data and understanding modern AI tools require a vast interdisciplinary mathematical toolbox. An intricate geometry governs most data science applications and optimization tasks. This talk highlights how nonlinear algebra can be used to investigate these geometric structures. The tools we discuss are algebraic geometry at heart but driven by applications and interactions with other mathematical disciplines. The applications we focus on in this talk are machine learning with neural networks, 3D reconstruction in computer vision, and maximum likelihood estimation in statistics.