https://arshadhs.github.io/tags/source-index/Recent content in Source Index on Arshad SiddiquiHugoen-ushttps://arshadhs.github.io/docs/ai/maths/mfml-topic-to-source-index/Mon, 01 Jan 0001 00:00:00 +0000https://arshadhs.github.io/docs/ai/maths/mfml-topic-to-source-index/<h1 id="mfml-topic-to-source-index"> MFML Topic to Source Index <a class="anchor" href="#mfml-topic-to-source-index">#</a> </h1> <p>This index tells you where to look when you want to create future notes or revise a topic.</p> <table> <thead> <tr> <th>Topic</th> <th>Primary source PDFs</th> <th>Supporting source PDFs</th> <th>Future Hugo page</th> </tr> </thead> <tbody> <tr> <td>Linear systems</td> <td>Lecture 1</td> <td>Webinar 1</td> <td><code>01-linear-systems-and-matrices.md</code></td> </tr> <tr> <td>Matrix operations</td> <td>Lecture 1</td> <td>Webinar 1</td> <td><code>01-linear-systems-and-matrices.md</code></td> </tr> <tr> <td>Vector spaces</td> <td>Lecture 2</td> <td>Webinar 1</td> <td><code>02-vector-spaces-subspaces-basis-rank.md</code></td> </tr> <tr> <td>Subspaces</td> <td>Lecture 2</td> <td>Webinar 1</td> <td><code>02-vector-spaces-subspaces-basis-rank.md</code></td> </tr> <tr> <td>Linear independence, span, basis</td> <td>Lecture 2</td> <td>Webinar 1</td> <td><code>02-vector-spaces-subspaces-basis-rank.md</code></td> </tr> <tr> <td>Rank and nullity</td> <td>Lecture 2</td> <td>Webinar 1</td> <td><code>02-vector-spaces-subspaces-basis-rank.md</code></td> </tr> <tr> <td>Norms and distances</td> <td>Lecture 3</td> <td>Webinar 1</td> <td><code>03-analytic-geometry-norms-inner-products.md</code></td> </tr> <tr> <td>Inner products</td> <td>Lecture 3</td> <td>Webinar 1</td> <td><code>03-analytic-geometry-norms-inner-products.md</code></td> </tr> <tr> <td>Orthogonality and Gram-Schmidt</td> <td>Lecture 3</td> <td>Webinar 1</td> <td><code>03-analytic-geometry-norms-inner-products.md</code></td> </tr> <tr> <td>Determinant and trace</td> <td>Lecture 4</td> <td>Webinar 1</td> <td><code>04-determinants-trace-eigenvalues.md</code></td> </tr> <tr> <td>Eigenvalues/eigenvectors</td> <td>Lecture 4</td> <td>Webinar 1, Webinar 2</td> <td><code>04-determinants-trace-eigenvalues.md</code></td> </tr> <tr> <td>Cholesky</td> <td>Lecture 4</td> <td>Webinar 1</td> <td><code>04-determinants-trace-eigenvalues.md</code></td> </tr> <tr> <td>Diagonalisation</td> <td>Lecture 5</td> <td>Webinar 2</td> <td><code>05-eigendecomposition-svd-matrix-approximation.md</code></td> </tr> <tr> <td>Eigendecomposition</td> <td>Lecture 5</td> <td>Webinar 2</td> <td><code>05-eigendecomposition-svd-matrix-approximation.md</code></td> </tr> <tr> <td>SVD</td> <td>Lecture 5</td> <td>Lecture 13, Webinar 1</td> <td><code>05-eigendecomposition-svd-matrix-approximation.md</code></td> </tr> <tr> <td>Differentiation</td> <td>Lecture 6</td> <td>Webinar 2</td> <td><code>06-vector-calculus-gradients.md</code></td> </tr> <tr> <td>Gradients</td> <td>Lecture 6, Lecture 7</td> <td>Webinar 2, Webinar 3</td> <td><code>06-vector-calculus-gradients.md</code></td> </tr> <tr> <td>Backpropagation</td> <td>Lecture 7</td> <td>—</td> <td><code>07-backpropagation-automatic-differentiation.md</code></td> </tr> <tr> <td>Automatic differentiation</td> <td>Lecture 7</td> <td>—</td> <td><code>07-backpropagation-automatic-differentiation.md</code></td> </tr> <tr> <td>Taylor/Maclaurin series</td> <td>Lecture 6, Lecture 8</td> <td>Webinar 2</td> <td><code>08-taylor-series-hessian-maxima-minima.md</code></td> </tr> <tr> <td>Hessian</td> <td>Lecture 8</td> <td>Webinar 2</td> <td><code>08-taylor-series-hessian-maxima-minima.md</code></td> </tr> <tr> <td>Maxima/minima</td> <td>Lecture 8</td> <td>Webinar 2</td> <td><code>08-taylor-series-hessian-maxima-minima.md</code></td> </tr> <tr> <td>Gradient descent</td> <td>Lecture 9</td> <td>Webinar 3</td> <td><code>09-gradient-descent-continuous-optimisation.md</code></td> </tr> <tr> <td>Step size / line search</td> <td>Lecture 9</td> <td>Webinar 3</td> <td><code>09-gradient-descent-continuous-optimisation.md</code></td> </tr> <tr> <td>Constrained optimisation</td> <td>Lecture 9, Lecture 14</td> <td>Webinar 4</td> <td><code>14-lagrangian-duality-kkt.md</code></td> </tr> <tr> <td>Lagrange multipliers</td> <td>Lecture 14</td> <td>Webinar 4</td> <td><code>14-lagrangian-duality-kkt.md</code></td> </tr> <tr> <td>KKT conditions</td> <td>Lecture 14, Lecture 15</td> <td>Webinar 4</td> <td><code>14-lagrangian-duality-kkt.md</code></td> </tr> <tr> <td>Feature preprocessing</td> <td>Lecture 10</td> <td>—</td> <td><code>10-nonlinear-optimisation-sgd-feature-preprocessing.md</code></td> </tr> <tr> <td>Overfitting</td> <td>Lecture 10</td> <td>—</td> <td><code>10-nonlinear-optimisation-sgd-feature-preprocessing.md</code></td> </tr> <tr> <td>SGD</td> <td>Lecture 10</td> <td>Webinar 3</td> <td><code>10-nonlinear-optimisation-sgd-feature-preprocessing.md</code></td> </tr> <tr> <td>Cliffs and valleys</td> <td>Lecture 11</td> <td>—</td> <td><code>11-momentum-adagrad-rmsprop-adam.md</code></td> </tr> <tr> <td>Momentum</td> <td>Lecture 11</td> <td>Webinar 3</td> <td><code>11-momentum-adagrad-rmsprop-adam.md</code></td> </tr> <tr> <td>AdaGrad, RMSProp, Adam</td> <td>Lecture 11</td> <td>—</td> <td><code>11-momentum-adagrad-rmsprop-adam.md</code></td> </tr> <tr> <td>PCA foundations</td> <td>Lecture 12</td> <td>Webinar 4</td> <td><code>12-pca-foundations.md</code></td> </tr> <tr> <td>PCA computation</td> <td>Lecture 13</td> <td>Webinar 4</td> <td><code>13-pca-practical-computation-svd.md</code></td> </tr> <tr> <td>Low-rank PCA</td> <td>Lecture 13</td> <td>Lecture 5</td> <td><code>13-pca-practical-computation-svd.md</code></td> </tr> <tr> <td>SVM preliminaries</td> <td>Lecture 14</td> <td>Webinar 4</td> <td><code>15-support-vector-machines.md</code></td> </tr> <tr> <td>Linear SVM</td> <td>Lecture 15</td> <td>Webinar 4</td> <td><code>15-support-vector-machines.md</code></td> </tr> <tr> <td>Hinge loss</td> <td>Lecture 15</td> <td>Webinar 4</td> <td><code>15-support-vector-machines.md</code></td> </tr> <tr> <td>Kernels / nonlinear SVM</td> <td>Lecture 14/15, possibly missing Lecture 16</td> <td>Webinar 4</td> <td><code>16-nonlinear-svm-kernels.md</code></td> </tr> </tbody> </table>