Advanced Linear Algebra with Applications - Part I (Numerical linear algebra for PDEs, machine learning, and data assimilation)
This material introduces advanced numerical linear algebra by showing how techniques used for solving partial differential equations are universally applicable to large-scale problems like machine learning and data assimilation. It focuses on using matrix-vector products, spectral properties, and Krylov subspace methods to solve systems too large to factorize directly.