MFML Topic to Source Index #
This index tells you where to look when you want to create future notes or revise a topic.
| Topic | Primary source PDFs | Supporting source PDFs | Future Hugo page |
|---|---|---|---|
| Linear systems | Lecture 1 | Webinar 1 | 01-linear-systems-and-matrices.md |
| Matrix operations | Lecture 1 | Webinar 1 | 01-linear-systems-and-matrices.md |
| Vector spaces | Lecture 2 | Webinar 1 | 02-vector-spaces-subspaces-basis-rank.md |
| Subspaces | Lecture 2 | Webinar 1 | 02-vector-spaces-subspaces-basis-rank.md |
| Linear independence, span, basis | Lecture 2 | Webinar 1 | 02-vector-spaces-subspaces-basis-rank.md |
| Rank and nullity | Lecture 2 | Webinar 1 | 02-vector-spaces-subspaces-basis-rank.md |
| Norms and distances | Lecture 3 | Webinar 1 | 03-analytic-geometry-norms-inner-products.md |
| Inner products | Lecture 3 | Webinar 1 | 03-analytic-geometry-norms-inner-products.md |
| Orthogonality and Gram-Schmidt | Lecture 3 | Webinar 1 | 03-analytic-geometry-norms-inner-products.md |
| Determinant and trace | Lecture 4 | Webinar 1 | 04-determinants-trace-eigenvalues.md |
| Eigenvalues/eigenvectors | Lecture 4 | Webinar 1, Webinar 2 | 04-determinants-trace-eigenvalues.md |
| Cholesky | Lecture 4 | Webinar 1 | 04-determinants-trace-eigenvalues.md |
| Diagonalisation | Lecture 5 | Webinar 2 | 05-eigendecomposition-svd-matrix-approximation.md |
| Eigendecomposition | Lecture 5 | Webinar 2 | 05-eigendecomposition-svd-matrix-approximation.md |
| SVD | Lecture 5 | Lecture 13, Webinar 1 | 05-eigendecomposition-svd-matrix-approximation.md |
| Differentiation | Lecture 6 | Webinar 2 | 06-vector-calculus-gradients.md |
| Gradients | Lecture 6, Lecture 7 | Webinar 2, Webinar 3 | 06-vector-calculus-gradients.md |
| Backpropagation | Lecture 7 | — | 07-backpropagation-automatic-differentiation.md |
| Automatic differentiation | Lecture 7 | — | 07-backpropagation-automatic-differentiation.md |
| Taylor/Maclaurin series | Lecture 6, Lecture 8 | Webinar 2 | 08-taylor-series-hessian-maxima-minima.md |
| Hessian | Lecture 8 | Webinar 2 | 08-taylor-series-hessian-maxima-minima.md |
| Maxima/minima | Lecture 8 | Webinar 2 | 08-taylor-series-hessian-maxima-minima.md |
| Gradient descent | Lecture 9 | Webinar 3 | 09-gradient-descent-continuous-optimisation.md |
| Step size / line search | Lecture 9 | Webinar 3 | 09-gradient-descent-continuous-optimisation.md |
| Constrained optimisation | Lecture 9, Lecture 14 | Webinar 4 | 14-lagrangian-duality-kkt.md |
| Lagrange multipliers | Lecture 14 | Webinar 4 | 14-lagrangian-duality-kkt.md |
| KKT conditions | Lecture 14, Lecture 15 | Webinar 4 | 14-lagrangian-duality-kkt.md |
| Feature preprocessing | Lecture 10 | — | 10-nonlinear-optimisation-sgd-feature-preprocessing.md |
| Overfitting | Lecture 10 | — | 10-nonlinear-optimisation-sgd-feature-preprocessing.md |
| SGD | Lecture 10 | Webinar 3 | 10-nonlinear-optimisation-sgd-feature-preprocessing.md |
| Cliffs and valleys | Lecture 11 | — | 11-momentum-adagrad-rmsprop-adam.md |
| Momentum | Lecture 11 | Webinar 3 | 11-momentum-adagrad-rmsprop-adam.md |
| AdaGrad, RMSProp, Adam | Lecture 11 | — | 11-momentum-adagrad-rmsprop-adam.md |
| PCA foundations | Lecture 12 | Webinar 4 | 12-pca-foundations.md |
| PCA computation | Lecture 13 | Webinar 4 | 13-pca-practical-computation-svd.md |
| Low-rank PCA | Lecture 13 | Lecture 5 | 13-pca-practical-computation-svd.md |
| SVM preliminaries | Lecture 14 | Webinar 4 | 15-support-vector-machines.md |
| Linear SVM | Lecture 15 | Webinar 4 | 15-support-vector-machines.md |
| Hinge loss | Lecture 15 | Webinar 4 | 15-support-vector-machines.md |
| Kernels / nonlinear SVM | Lecture 14/15, possibly missing Lecture 16 | Webinar 4 | 16-nonlinear-svm-kernels.md |