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teaching:ws2019:cvx4cv [2019/10/14 14:03] Zhenzhang Ye |
teaching:ws2019:cvx4cv [2020/06/11 13:43] (current) wuta |
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< | < | ||
- | <b> Announcement: | + | <b> Announcement: |
- | <html>< | + | <b>< |
+ | <br /> | ||
+ | The retake exam is unfortunately canceled due to the Coronavirus outbreak. | ||
+ | <br /> | ||
+ | The location of the final exam is updated. <br /> | ||
+ | There is no lecture on 23.12. The tutorial session on 08.01 will be replaced by the lecture. | ||
+ | <br /> | ||
+ | There will be no lecture/ | ||
+ | The tutorial on 30.10 will be given by Yuesong Shen | ||
+ | <br /> | ||
+ | The first lecture | ||
</ | </ | ||
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Elements in convex analysis | Elements in convex analysis | ||
- | * Convex | + | * Convex |
* Existence and uniqueness of minimizers | * Existence and uniqueness of minimizers | ||
- | | + | |
- | * Convex | + | * Convex |
- | | + | |
+ | * Moreau envelope | ||
| | ||
Numerical methods | Numerical methods | ||
* Gradient-based methods | * Gradient-based methods | ||
- | * Proximal algorithms, primal-dual hybrid gradient method, alternating direction method of multipliers | + | * Proximal algorithms: primal-dual hybrid gradient method, alternating direction method of multipliers |
- | | + | |
* Acceleration techniques | * Acceleration techniques | ||
- | |||
Examplary applications in machine learning and computer vision include | Examplary applications in machine learning and computer vision include | ||
- | * Training of SVMs, Logistic regression | + | * Logistic regression |
- | * Image reconstruction (e.g. denoising, deblurring, inpainting) | + | * Training of SVMs |
+ | * Image reconstruction (e.g. denoising, inpainting, segmentation) | ||
* Low-rank and sparse matrix decomposition | * Low-rank and sparse matrix decomposition | ||
We will implement some of them in Python and MATLAB. | We will implement some of them in Python and MATLAB. | ||
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== Lecture == | == Lecture == | ||
** Location:** Room 02.09.023\\ | ** Location:** Room 02.09.023\\ | ||
- | ** Time and Date:** Monday 16:15 - 18: | + | ** Time and Date:** Monday 16:15 -- 18: |
** Start:** October 21st, 2019 \\ | ** Start:** October 21st, 2019 \\ | ||
** Lecturer:** [[: | ** Lecturer:** [[: | ||
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== Exercise == | == Exercise == | ||
** Location:** Room 02.09.023\\ | ** Location:** Room 02.09.023\\ | ||
- | ** Time and Date:** Wednesday 12:15 - 14: | + | ** Time and Date:** Wednesday 12:15 -- 14: |
** Start:** October 23rd, 2019\\ | ** Start:** October 23rd, 2019\\ | ||
** Organization: | ** Organization: | ||
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1. get at least 75% grades of all exercises totally, i.e. sum of the grades you get in all exercises divided by the grades of all exercises (without bonus) should be >= 0.75\\ | 1. get at least 75% grades of all exercises totally, i.e. sum of the grades you get in all exercises divided by the grades of all exercises (without bonus) should be >= 0.75\\ | ||
2. present your theoretical solution during tutorial at least once in this semester.\\ | 2. present your theoretical solution during tutorial at least once in this semester.\\ | ||
- | You cannot improve either 1.0 or >4.0.\\ | + | You cannot improve either 1.0 or 5.0.\\ |
== Exam == | == Exam == | ||
- | tba | + | Date: February 28th, 17:00 - 18:30. \\ |
+ | Place: MW 0350, Egbert-von-Hoyer-Hörsaal (5503.EG.350). \\ | ||
+ | The final exam will be written. You are allowed to bring one A4-page, handwritten cheat sheet to the exam. | ||
+ | == Retake Exam == | ||
+ | Date: July 8th, 10:45 - 12:15. \\ | ||
+ | Place: 0.002, Tentomax HS2 (5538.EG.002) \\ | ||
+ | The exam is written. You are allowed to bring one A4-size, double-sided, | ||
- | == Lecture | + | == Lecture |
- | Course | + | Course |