Lecturer(s)
|
-
Šmídl Václav, Prof. Ing. Ph.D.
-
Ševčík Jakub, Ing.
|
Course content
|
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.
|
Learning activities and teaching methods
|
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
|
prerequisite |
---|
Knowledge |
---|
independently use elementary knowledge of mathematical analysis, linear algebra, probability and statistics |
develop simple mathematical models using the laws of physics |
Skills |
---|
program in higher-level computer languges, such as C++, C#, MATLAB or python |
studovat odbornou literaturu v anglickém jazyce |
Competences |
---|
N/A |
learning outcomes |
---|
Knowledge |
---|
using selected methods of machine learning for developing mathematical models from data |
use the mathematical model for optimization of the physical system |
Skills |
---|
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 |
---|
N/A |
N/A |
teaching methods |
---|
Knowledge |
---|
Lecture |
Interactive lecture |
Skills |
---|
Practicum |
Project-based instruction |
Competences |
---|
Task-based study method |
assessment methods |
---|
Knowledge |
---|
Project |
Continuous assessment |
Skills |
---|
Project |
Competences |
---|
Continuous assessment |
Recommended literature
|
-
Bishop, Christopher M. Pattern recognition and machine learning. New York : Springer, 2006. ISBN 0-387-31073-8.
|