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Lecturer(s)
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Šmídl Václav, Prof. Ing. Ph.D.
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Ševčík Jakub, Ing.
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Course content
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1 Introduction and aims of the course. Julia language primer: syntax, control and data structures, primitive types. 2 Type system of Julia, multiple-dispatch, macros and generated code 3 Code organization in the Julia language, use and creation of the packages, visualization 4 Elements of machine learning demonstrated on lineární regression: optimization, train/test data, model selection and comparison 5 Optimization techniques, gradient descent and its variants, constraint optimization, local minima. Package Optim. 6 Automatic differentiation, forward and backward regime. Source-to-source differentiation. Packages ForwardDiff, ReverseDiff, Zygote 7 Neural networks as a universal approximator. Basic architectures and examples of their use. Package Flux. 8 Learning of dynamical systems. Linear systems, nonlinear systems, nelineární (Sindy method) and recurrent neural networks. 9 Neural differential equations, ordinary and partial. Physics-informed neural networks. Packages DataDrivenDiffEq a SciML. 10 Analytical probabilistic methods. Gaussian least squares methods, Gaussian processes. Package AbstractGP. 11 Monte Carlo methods with a focus on Hamiltonovské MC, probabilistic programming. Package Turing. 12 Design of experiments, development of a solution for linear models, Bayesian optimization.
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Learning activities and teaching methods
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Laboratory work, Lecture
- Contact hours
- 52 hours per semester
- Preparation for laboratory testing; outcome analysis (1-8)
- 10 hours per semester
- Individual project (40)
- 40 hours per semester
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| prerequisite |
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| Knowledge |
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| independently use elementary knowledge of mathematical analysis, linear algebra, probability and statistics |
| develop simple mathematical models using the laws of physics |
| Skills |
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| program in higher-level computer languges, such as C++, C#, MATLAB or python |
| studovat odbornou literaturu v anglickém jazyce |
| Competences |
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| N/A |
| learning outcomes |
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| Knowledge |
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| using selected methods of machine learning for developing mathematical models from data |
| use the mathematical model for optimization of the physical system |
| Skills |
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| elementary use of the Julia language |
| use existing tools for practical applications |
| apply machine learning methods for faster innovation and research in the electrical engineering |
| Competences |
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| N/A |
| N/A |
| teaching methods |
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| Knowledge |
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| Lecture |
| Interactive lecture |
| Skills |
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| Practicum |
| Project-based instruction |
| Competences |
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| Task-based study method |
| assessment methods |
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| Knowledge |
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| Project |
| Continuous assessment |
| Skills |
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| Project |
| Competences |
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| Continuous assessment |
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Recommended literature
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Bishop, Christopher M. Pattern recognition and machine learning. New York : Springer, 2006. ISBN 0-387-31073-8.
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