Mathematical Methods of Machine Learning and Data Science
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.
- Lectures: Prof. Christoph Schnörr
- Exercises: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