Numerical linear algebra course, AI Masters, Fall 2023 Date Lectures Practice sessions Home assignments 18.09.2023 General info about the course. Floating point numbers. Example of ResNet model and different formats of floating point numbers 25.09.2023 Vector and matrix norms. Unitary matrices and algorithms stability 02.10.2023 Linear systems and condition number Comparison of different approaches to solve linear systems 09.10.2023 Matrix rank. Low-rank matrix approximation and Singular Value Decomposition (SVD). SVD for recommender systems 16.10.2023 Methods for computing QR decomposition. Eigenvalue decomposition. Power method convergence HW1 Deadline: November 2, 23:50 MSK 23.10.2023 QR algorithm. Overview of methods for computing SVD 30.10.2023 Matrix functions, vol. 1 06.11.2023 Projects presentations, vol. 1 HW2 Deadline: November 29, 23:50 MSK 20.11.2023 Sparse matrices and where to meet them. LU for sparse matrices. 27.11.2023 Intro to iterative methods for linear systems. Krylov methods. 11.12.2023 Intro to preconditioners. Matrix functions, vol. 2. 18.12.2023 Intro to tensors. Tensor decompositions and why are they important 25.12.2023 Projects presentations