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This course covers advanced topics in Machine Learning with a focus on physical applications, extending beyond
standard introductory material. Each lesson combines theoretical insights with practical, hands-on examples and
group discussions. It is intended for students who are already familiar with the fundamentals of machine learning
and are interested in exploring its advanced uses in physics and related scientific fields.
Last update: Mikšová Kateřina, Mgr. (12.06.2025)
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The goal of the course is to provide students with an overview of current challenges in scientific research and selected modern machine learning techniques used to address them. Last update: Mikšová Kateřina, Mgr. (12.06.2025)
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To receive credit for the course, active participation is required. This includes regular attendance and engagement in lectures and discussions. Each student will be assigned a machine learning project, which will be developed throughout the semester and discussed during the practical sessions. The final evaluation will be based on the successful completion of the project and a 20-minute presentation at the end of the semester. Last update: Mikšová Kateřina, Mgr. (12.06.2025)
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1. Schutt et al.: Machine Learning Meets Quantum Physics, Springer (2020). 2. Kilpatrick: Deep Learning with Julia: deeplearningwithjulia.com. 3. F. Chollet: Deep learning v jazyku Python. Knihovny Keras, Tensorflow. Grada (2019). 4. P. Mehta, M. Bukov, C.-H. Wang, A. G. R. Day, C. Richardson, C. K. Fisher, and D. J. Schwab, A High-Bias: Low-Variance Introduction to Machine Learning for Physicists, Physics Reports 810, 1 (2019). 5. Anna Dawid et al.: Modern applications of machine learning in quantum sciences, arXiv:2204.04198 [quant-ph] (2022). 6. A. Tanaka,A .Tomiya, K. Hashimoto: Deep Learning and Physics, Springer Verlang (2021) 7. G. Carleo et al., Machine Learning and the Physical Sciences, Rev. Mod. Phys. 91, 045002 (2019). Last update: Mikšová Kateřina, Mgr. (22.12.2022)
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1. Recent topics of Machine Learning in Science
2. Interpretable Machine Learning
3. Automatic differentiation for Machine Learning in programming languages Python and Julia
4. Neuromorfic computing. Basic concepts and current state of the research in the field TensorFlow Quantum. Machine Learning and Quantum computing
5. Physics-informed Neural Networks
6. Neural Differential Equations
7. Large Language Models, Transformers
8. Diffusion models Last update: Mikšová Kateřina, Mgr. (12.06.2025)
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