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TUT 1: Physics Meets Machine Learning (joint session DY/TUT/TT)
Sonntag, 26. März 2023, 16:00–18:15, HSZ 01
Machine learning has revolutionized many application fields such as computer vision and natural language processing. In physics there is a growing interest in using machine learning to enhance the analysis of experimental data and to devise and optimize experiments or numerical simulations. On the other hand physicists use their intuition and methods from statistical physics and complex systems theory to better understand the working principles of modern machine learning methods. This tutorial session introduces some subfields within this area and the basic methods involved.
Organized by Sabine Andergassen (Tübingen), Martin Gärttner (Heidelberg), Moritz Helias (Jülich), and Markus Schmitt (Cologne)
|16:00||TUT 1.1||Tutorium: Machine Learning for Quantum Technologies — •Florian Marquardt|
|16:45||TUT 1.2||Tutorium: The Unreasonable Effectiveness of Gaussians in the Theory of Deep Neural Networks — •Zohar Ringel|
|17:30||TUT 1.3||Tutorium: Computing learning curves for large machine learning models using the replica approach — •Manfred Opper|