Numerical Linear Algebra in AI Masters, Fall 2022 Date Lectures Practice sessions Home assignments 15.09.2022 General info about the course. Floating point numbers. Vector norms Review and main policies HW1 (Deadline: October, 4, 23:59 MSK) 22.09.2022 Matrix norms and unitary matrices Seminar 2 29.09.2022 Seminar 3 Seminar 4 HW2 (Deadline: October, 11, 23:59 MSK) 06.10.2022 Matrix rank and low-rank approximation. SVD. Linear systems 13. 10.2022 Matrix multiplication and memory hierarchy. Seminar 5 HW3 (Deadline: October, 25, 23:59 MSK) 20.10.2022 Seminar 6 Seminar 7 27.10.2022 QR decomposition and how to compute it. Eigenvalues and eigenvectors. Schur decomposition. QR algorithm. SVD and how we compute it. 03.11.2022 Seminars 8 and 9 10.11.2022 Sparse matrices and direct methods for large sparse linear systems. Spectral partitioning and Fiedler vector Intro to iterative methods 17.11.2022 Great iterative methods Seminar 10 24.11.2022 Seminar 11 Seminar 12 HW4 (Deadline: December, 4, 23:59 MSK) 01.12.2022 Iterative methods and preconditioners Seminar 13 08.12.2022 Iterative methods for partial eigenvalue problem Seminar 14 HW5 (Deadline: December, 15, 23:59 MSK) 16.12.2022 Structured matrices, convolutions, FFT, Toeplitz matrices Matrix functions and randomized methods in NLA