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Last update: Mgr. Kateřina Mikšová (13.05.2022)
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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. |
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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.
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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). |
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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. 5. Feed forward neural networks. Supervised learning. Backpropagation algorithm. 6. Unsupervised learning of neural networks. Hopfield neural networks. Boltzmann machines. Restricted Boltzmann machines. Autoencoders. Automatic phase classification. 7. Deep learning. Convolutional neural networks. Neural network regularization. Image recognition. 8. Analysis and forecasting of time sequences. Arima model. Recurrent neural networks. LSTM and GRU memory cells. 9. Application of neural networks in quantum physics. Neural network quantum states and quantum state tomography. 10. Neuromorfic computing. Basic concepts and current state of the research in the field.
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