Explore advanced concepts in machine learning and control theory through this 51-minute lecture from the 2019 ADSI Summer Workshop. Delve into "Better Gaussian Kernel Factorization via Approximation Theory, with Applications" presented by Pablo Parrilo from MIT. Learn about optimal transport matrix scaling, control entrywise errors, and key ideas in approximation with polynomials. Discover plane wave expansion techniques, modern quadratic factors, and harmonic polynomials. Examine the product space of features and empirical results in linear classification. Gain insights into cutting-edge algorithmic foundations that bridge learning and control systems.