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teaching:st20:lecture [2020/07/24 10:31]
teaching:st20:lecture [2021/03/04 09:55] (current)
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 +====== Mathematical Methods of Image and Pattern Analysis ======
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 +**<color #​ed1c24>​Please ask any questions that might be of general interest in the [[https://​moodle.uni-heidelberg.de/​mod/​forum/​view.php?​id=25764|feedback forum]] instead of by mail.</​color>​**
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 +**Please go to [[https://​moodle.uni-heidelberg.de/​course/​view.php?​id=1942|Moodle]] for further information and the teaching material.**
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 +  * **Lectures:​** [[:​people|Prof. Christoph Schnörr]]
 +  * **Exercises:​** [[:​people|Alexander Zeilmann]]
 +  * **Format:**
 +      * Extensive lecture notes that you read on your own.
 +      * Short videos that explain each subtopic from a top-down viewpoint. This is not a replacement for reading the lecture notes.
 +      * Exercise sheets you work on your own and compare with our solution videos
 +      * Mulitple Choice tests
 +      * A [[https://​moodle.uni-heidelberg.de/​mod/​forum/​view.php?​id=25764|feedback forum in Moodle]] where students can give feedback and ask questions.
 +      * No meeting in HeiConf, etc.
 +      * No in-person lecture
 +  * **Language:​** English
 +  * **SWS:** 4
 +  * **ECTS:** 6
 +  * **Lecture Id:** MM35, Spezialisierungsmodul Numerik und Optimierung
 +  * **Supplementary Practical:​** For doing Programming Exercises in May, June and July you get two extra credits (this might depend on your field of study.)
 +  * **Registration:​** Please register in [[https://​muesli.mathi.uni-heidelberg.de/​lecture/​view/​1179|Müsli]] and [[https://​moodle.uni-heidelberg.de/​course/​view.php?​id=1942|Moodle]] (you do not need an enrollment key).
 +  * **Feedback:​** We have a [[https://​moodle.uni-heidelberg.de/​mod/​forum/​view.php?​id=25764|feedback forum in Moodle]] for all questions related to the mathematical content and the organization of the course.
 +  * **Prior Knowledge:​** Required: Lineare Algebra and Analysis, Recommended:​ Convex and Nonlinear Optimization
 +  * **Content:​** Discrete and Continuous Fourier Transformations,​ FIR Filter, Reproducing Kernel Hilbert Spaces, Markov Random Fields, Gaussian Markov Random Fields, Exponential Family, Elementary Differential Geometry
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