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teaching:ws2019:cvx4cv [2019/10/10 12:00] Zhenzhang Ye created |
teaching:ws2019:cvx4cv [2019/10/20 01:38] wuta |
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* Image reconstruction (e.g. denoising, deblurring, inpainting) | * Image reconstruction (e.g. denoising, deblurring, inpainting) | ||
* Low-rank and sparse matrix decomposition | * Low-rank and sparse matrix decomposition | ||
- | We will implement some of them in MATLAB. | + | We will implement some of them in Python and MATLAB. |
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We will give you back the corrected sheets on Wednesday when we discuss them in class.\\ | We will give you back the corrected sheets on Wednesday when we discuss them in class.\\ | ||
Please remember to write clean, commented(!) code! You are allowed to work on the exercise sheets in groups of two students. \\ | Please remember to write clean, commented(!) code! You are allowed to work on the exercise sheets in groups of two students. \\ | ||
- | The first exercise sheet that counts to the exam bonus is exercise sheet 1, which is due Monday, 29th of October. \\ | ||
The exercise sheets can be accessed [[teaching: | The exercise sheets can be accessed [[teaching: | ||
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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 == | ||
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- | == Lecture | + | == Lecture |
- | Course | + | Course |