Mathematical Methods of Machine Learning and Data Science

The lecture introduces basic mathematical methods required to understand both classical approaches and their connection to the ingredients of deep learning architectures: convolution and mathematical signal processing, data embedding and the impact of high dimensions, randomization and concentration of measure, measure transport, elementary Riemannian geometry and flows realized by networks.