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- | ====== Lecture: Introduction to Neural ODEs and Assignment Flows for Machine Learning ====== | + | ====== Lecture: Convex Optimization and Machine Learning ====== |
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- | content is coming soon! | + | |
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- | ==== Exercise Sheets: ==== | + | |
+ | * **Lectures:** [[:people|Prof. Christoph Schnörr]] | ||
+ | * **Exercises:** [[:people|Daniel Gonzalez-Alvarado]] and [[:people|Jonathan Schwarz]] | ||
+ | * **Language:** English | ||
+ | * **SWS:** 2+2 | ||
+ | * **ECTS:** 6 | ||
+ | * **Lecture Id:** MM35, Spezialisierungsmodul Numerik und Optimierung | ||
+ | * **Registration:** Please register in [[https://muesli.mathi.uni-heidelberg.de/lecture/view/1696|Müsli]] | ||
+ | * **Prior Knowledge:** Foundational courses on Linear Algebra and Analysis | ||
+ | * **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//. | ||
+ | * Some short **lecture notes** and the **exercise sheets** can be found [[https://heibox.uni-heidelberg.de/d/dc218220b9fb4a7894fb/|here]]. |