The Mathematical Universe behind Deep Neural Networks

Deep neural networks have led to breakthrough results in numerous practical machine learning tasks. In this lecture, we will attempt a journey through the mathematical universe behind these practical successes, elucidating the theoretical underpinnings of deep neural networks in functional analysis, harmonic analysis, complex analysis, approximation theory, dynamical systems, Kolmogorov complexity, optimal transport, fractal geometry, mathematical logic, and automata theory.