# Past lectures

You can easily run all the notebooks right in your browser through the Binder

You can download the full zip archive of all course materials

slides page markdown notebook
Python Introduction .html .html .md .ipynb
TV denoising demo .html .html .md .ipynb
1 Floating-point arithmetic, vector norms .html .html .md .ipynb
2 Matrix norms and unitary matrices .html .html .md .ipynb
3 Matvecs and matmuls, memory hierarchy, Strassen algorithm .html .html .md .ipynb
4 Matrix rank, low-rank approximation, SVD .html .html .md .ipynb
5 Linear systems .html .html .md .ipynb
Matrix Calculus .html .html .md .ipynb
6 Eigenvalues and eigenvectors .html .html .md .ipynb
7 Matrix decompositions and how we compute them .html .html .md .ipynb
8 Symmetric eigenvalue problem and SVD .html .html .md .ipynb
9 From dense to sparse linear algebra .html .html .md .ipynb
10 Sparse direct solvers .html .html .md .ipynb
11 Intro to iterative methods .html .html .md .ipynb
12 Great Iterative Methods .html .html .md .ipynb
13 Iterative methods and preconditioners .html .html .md .ipynb
14 Iterative methods for large scale eigenvalue problems .html .html .md .ipynb
15 Structured matrices, FFT, convolutions, Toeplitz matrices .html .html .md .ipynb
16 Matrix functions and matrix equations .html .html .md .ipynb
17 Tensors and tensor decompositions .html .html .md .ipynb