The lecture will provide a practical introduction into basic numerical optimization techniques and machine learning
methods used in classical and quantum physics as well as in other fields of science. The most important methods
will be analyzed in detail during the exercises in a form of hands-on sessions and projects by using the Python
libraries Scikit-learn, sktime, Tensorflow, Keras, and NetKet.
Last update: Mgr. Kateřina Mikšová (13.05.2022)
V přednášce budou vysvětleny základní optimalizační techniky a metody strojového učení a jejich využití v klasické
a kvantové fyzice a jiných přírodních vědách. Nejdůležitější metody budou detailněji analyzované na cvičeních,
kde budou použity knihovny Scikit-learn, sktime, Tensorflow, Keras a NetKet v programovacím jazyku Python.
Aim of the course -
Last update: Mgr. Kateřina Mikšová (13.05.2022)
The aim of the course is to master the basic optimization techniques and methods of machine learning for use in physics and other fields of science.
Last update: Mgr. Kateřina Mikšová (13.05.2022)
Cílem předmětu je osvojit si základní optimalizační techniky a metódy strojového učení s využitím ve fyzice a jiných přírodních vědách.
Course completion requirements -
Last update: Mgr. Kateřina Mikšová (13.05.2022)
To obtain the credit, which is a condition for admission to the exam, it is necessary to collect at least 65% of points from the assignments. The questions in the exam are based on the syllabus.
Last update: Mgr. Kateřina Mikšová (13.05.2022)
Na získání zápočtu, který je podmínkou k připuštění k zkoušce, je potřeba nasbírat alespoň 65% bodů z úloh zadaných na cvičení. Otázky na zkoušce vychází ze sylabu.
Literature -
Last update: Mgr. Kateřina Mikšová (13.05.2022)
1. V. Kvasnička: Evolučné algoritmy, STU Bratislava (2000).
2. F. Chollet: Deep learning v jazyku Python. Knihovny Keras, Tensorflow. Grada (2019).
3. T. M. Mitchell: Machine learning, McGraw-Hill Science/Engineering/Math (1997).
4. A. Geron: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O'Reilly Media (2019).
5. 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).
6. Anna Dawid et al.: Modern applications of machine learning in quantum sciences, arXiv:2204.04198 [quant-ph] (2022).
Last update: Mgr. Kateřina Mikšová (13.05.2022)
1. V. Kvasnička: Evolučné algoritmy, STU Bratislava (2000).
2. F. Chollet: Deep learning v jazyku Python. Knihovny Keras, Tensorflow. Grada (2019).
3. T. M. Mitchell: Machine learning, McGraw-Hill Science/Engineering/Math (1997).
4. A. Geron: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O'Reilly Media (2019).
5. 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).
6. Anna Dawid et al.: Modern applications of machine learning in quantum sciences, arXiv:2204.04198 [quant-ph] (2022).
Syllabus -
Last update: Mgr. Kateřina Mikšová (16.05.2022)
1. Crash course in Python and its libraries NumPy, SciPy, and pandas.
2. Optimization problems in physics and their solutions. Gradient methods. Stochastic optimization algorithms (hill climbing and evolutionary algorithms).
3. Basics of Monte Carlo methods. Ising and Heisenberg model. Metropolis algorithm, heat bath algorithm. Ergodic theorem, detailed balance condition. Simulated annealing.
4. Basic methods in machine learning. Linear regression, logistic regression, support vector machines, decision trees, random forests.