Syllabus
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.
Annotation
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.