AMATH 345 - Data-Driven Mathematical Models

An introduction to data-driven mathematical methods for modelling and prediction of complex systems in science, medicine, and technology. Topics include singular value decomposition, sparsity and compressed sensing, calibration and parameter inference for differential equation models, as well as neural networks and data-driven methods for dynamical systems. Throughout the course, students will learn to use modern data science methods and apply recent advances in data-driven methods to a wide range of applications.