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| ====== Lecture: Convex Optimization and Machine Learning ====== | ====== Lecture: Convex Optimization and Machine Learning ====== | ||
| - | ====== Mathematical Methods of Image and Pattern Analysis ====== | ||
| * **Lectures:** [[:people|Prof. Christoph Schnörr]] | * **Lectures:** [[:people|Prof. Christoph Schnörr]] | ||
| - | * **Exercises:** [[:people|Daniel]] and [[:people|Jonathan]] | + | * **Exercises:** [[:people|Daniel Gonzalez-Alvarado]] and [[:people|Jonathan Schwarz]] |
| - | * **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 | * **Language:** English | ||
| - | * **SWS:** 4 | + | * **SWS:** 2+2 |
| * **ECTS:** 6 | * **ECTS:** 6 | ||
| * **Lecture Id:** MM35, Spezialisierungsmodul Numerik und Optimierung | * **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/1696|Müsli]] |
| - | * **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). | + | * **Prior Knowledge:** Foundational courses on Linear Algebra and Analysis |
| - | * **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. | + | * **Content:** The first part of this 2h-lecture is a crash course in //Convex Analysis and Optimization// with numerous applications to //Numerical Optimization// in general, //Machine Learning// and beyond. The last part of the lecture gives an introduction to the emerging research field //Learning to Optimize//. |
| - | * **Prior Knowledge:** Required: Lineare Algebra and Analysis, Recommended: Convex and Nonlinear Optimization | + | * Some short **lecture notes** and the **exercise sheets** can be found [[https://heibox.uni-heidelberg.de/d/dc218220b9fb4a7894fb/|here]]. |
| - | * **Content:** Discrete and Continuous Fourier Transformations, FIR Filter, Reproducing Kernel Hilbert Spaces, Markov Random Fields, Gaussian Markov Random Fields, Exponential Family, Elementary Differential Geometry | + | |