|
|
|
||
|
We will address several advanced topics of Machine Learning for physical applications which go beyond standard
introductory courses. Each lesson will consist of a 45-60 minute long lecture followed by practical examples and
discussion. The students will be encouraged to prepare a short talk, essay or a worksheet (i.e. a jupyter notebook)
on a particular topic. The seminar is intended for students familiar with the basics of machine learning techniques
who are interested in current development in the field.
Last update: Mikšová Kateřina, Mgr. (22.12.2022)
|
|
||
|
The aim of the course is to give students an overview of the current problems and chosen modern machine learning techniques used in science. Last update: Mikšová Kateřina, Mgr. (22.12.2022)
|
|
||
|
To obtain the credit it is necessary to participate actively at the seminars. This means to attend the lectures, participate in the discussions and to prepare a short talk, essay or a worksheet (i.e. a jupyter notebook) on a particular topic. Last update: Mikšová Kateřina, Mgr. (22.12.2022)
|
|
||
|
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)
|
|
||
|
1. Automatic differentiation for Machine learning in programming languages Python and Julia. 2. Neuromorfic computing. Basic concepts and current state of the research in the field. 3. TensorFlow Quantum. 4. Neural network quantum states beyond RBM. 5. Automatic phase classification via network confusion. 6. Physics-informed neural networks. 7. Graph neural networks. Last update: Mikšová Kateřina, Mgr. (22.12.2022)
|