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martinjaggi committed Feb 24, 2022
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Expand Up @@ -34,19 +34,19 @@ Non-Convex Optimization: Convergence to Critical Points, Alternating minimizatio
| Nr | Date | Topic | Materials | Exercises |
| --- | ----- | ----------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- | ------------------------------------- |
| #1 | 25.2. | Introduction, Convexity | [notes](../../raw/master/lecture_notes/lecture-notes.pdf), [slides](../../raw/master/slides/lecture01.pdf) | [lab01](../../tree/master/labs/ex01/) |
| #2 | 4.3. | Gradient Descent | [notes](../../raw/master/lecture_notes/lecture-notes.pdf), [slides](../../raw/master/slides/lecture02.pdf) | [lab02](../../tree/master/labs/ex02/) |
| #3 | 11.3. | Projected Gradient Descent | [notes](../../raw/master/lecture_notes/lecture-notes.pdf), [slides](../../raw/master/slides/lecture03.pdf) | [lab03](../../tree/master/labs/ex03/) |
| #4 | 18.3. | Proximal and Subgradient Descent | [notes](../../raw/master/lecture_notes/lecture-notes.pdf), [slides](../../raw/master/slides/lecture04.pdf) | [lab04](../../tree/master/labs/ex04/) |
| #5 | 25.3. | Stochastic Gradient Descent, Non-Convex Optimization | [notes](../../raw/master/lecture_notes/lecture-notes.pdf), [slides](../../raw/master/slides/lecture05.pdf) | [lab05](../../tree/master/labs/ex05/) |
| . | 1.4. | `easter vacation` | | - |
| . | 8.4. | `easter vacation` | | - |
| #6 | 15.4. | Non-Convex Optimization, Accelerated Gradient Descent | [notes](../../raw/master/lecture_notes/lecture-notes.pdf), [slides](../../raw/master/slides/lecture06.pdf) | [lab06](../../tree/master/labs/ex06/) |
| #7 | 22.4. | Newton's Method & Quasi-Newton | [notes](../../raw/master/lecture_notes/lecture-notes.pdf), [slides](../../raw/master/slides/lecture07.pdf) | [lab07](../../tree/master/labs/ex07/) |
| #8 | 29.4. | Frank-Wolfe | notes, [slides](../../raw/master/slides/lecture08.pdf) | [lab08](../../tree/master/labs/ex08/) |
| #9 | 6.5. | Coordinate Descent | [notes](../../raw/master/lecture_notes/lecture-notes.pdf), [slides](../../raw/master/slides/lecture09.pdf) | [lab09](../../tree/master/labs/ex09/) |
| #10 | 13.5. | Duality, Gradient-free, adaptive methods | notes, [slides](../../raw/master/slides/lecture10.pdf) | [lab10](../../tree/master/labs/ex10/) |
| #11 | 27.5. | Opt for ML in Practice | notes, [slides](../../raw/master/slides/lecture11.pdf) | Q&A |
| #12 | 3.6. | Opt for ML in Practice | notes, [slides](../../raw/master/slides/lecture12.pdf) | Q&A Projects |
| #2 | 4.3. | Gradient Descent | [notes](../../raw/master/lecture_notes/lecture-notes.pdf), | |
| #3 | 11.3. | Projected Gradient Descent | [notes](../../raw/master/lecture_notes/lecture-notes.pdf), | |
| #4 | 18.3. | Proximal and Subgradient Descent | [notes](../../raw/master/lecture_notes/lecture-notes.pdf), | |
| #5 | 25.3. | Stochastic Gradient Descent, Non-Convex Optimization | [notes](../../raw/master/lecture_notes/lecture-notes.pdf), | |
| #6 | 1.4. | Non-Convex Optimization, Accelerated Gradient Descent | [notes](../../raw/master/lecture_notes/lecture-notes.pdf), | |
| #7 | 8.4. | Newton's Method & Quasi-Newton | [notes](../../raw/master/lecture_notes/lecture-notes.pdf), | |
| . | 15.4. | `easter vacation` | | - |
| . | 22.4. | `easter vacation` | | - |
| #8 | 29.4. | Frank-Wolfe | notes, | |
| #9 | 6.5. | Coordinate Descent | [notes](../../raw/master/lecture_notes/lecture-notes.pdf), | |
| #10 | 13.5. | Duality, Gradient-free, adaptive methods | notes, | |
| #11 | 27.5. | Opt for ML in Practice | notes, | Q&A |
| #12 | 3.6. | Opt for ML in Practice | notes, | Q&A Projects |
| #13 | 10.6. | `Mini-Project week` | |

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