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teaching:wt24:vl:mathmethml [2024/09/26 20:21] ipa [Abstract of the Lecture] |
teaching:wt24:vl:mathmethml [2024/09/26 20:25] (current) ipa [Abstract of the Lecture:] |
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| ====== Mathematical Methods of Machine Learning and Data Science ====== | ====== Mathematical Methods of Machine Learning and Data Science ====== | ||
| - | ==== Abstract of the Lecture ==== | + | ==== Abstract of the Lecture: ==== |
| The lecture introduces basic mathematical methods required to understand both classical approaches and their connection to the ingredients of deep learning architectures: convolution and mathematical signal processing, data embedding and the impact of high dimensions, randomization and concentration of measure, measure transport, elementary Riemannian geometry and flows realized by networks. | The lecture introduces basic mathematical methods required to understand both classical approaches and their connection to the ingredients of deep learning architectures: convolution and mathematical signal processing, data embedding and the impact of high dimensions, randomization and concentration of measure, measure transport, elementary Riemannian geometry and flows realized by networks. | ||
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| + | * **Lectures:** [[:people|Prof. Christoph Schnörr]] | ||
| + | * **Exercises:**[[:people|Jonathan Schwarz]] | ||
| + | * **Language:** English | ||
| + | * **SWS:** 4 | ||
| + | * **ECTS:** 8 | ||
| + | * **Lecture Id:** MM35, Spezialisierungsmodul Numerik und Optimierung | ||
| + | * **Registration:**Registration will be announced | ||
| + | * **Prior Knowledge:** Foundational courses on Linear Algebra and Analysis | ||
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