The course covers Python basics, basics of object-oriented programming, and advanced topics such as data processing, mechanical system simulations, parallel programming, and artificial intelligence. It is structured into weekly modules focusing on different Python applications using well-known libraries like NumPy, SciPy, Pandas, Matplotlib, TensorFlow, Requests and FastAPI.

1. Getting acquainted with the Python environment. Installation, IDEs, basic syntax, data types, functions.
2. Python and object-oriented programming: classes and objects, methods, attributes, inheritance, polymorphism, magic methods.
3. Python for scientific computing using the NumPy, SciPy libraries - linear algebra, solving differential equations. Time series visualization using the Matplotlib library.
4. Working with data using the Pandas library. Loading data from a file, preprocessing, data operations such as sorting, filtering, basic statistics.
5. Simulation of mechanical systems.
6. Control of mechanical systems.
7. Optimization – linear and quadratic programming.
8. Parallel programming (multithreading).
9. Introduction to artificial intelligence (genetic algorithms, fuzzy systems, perceptron).
10. Artificial intelligence using the TensorFlow library - neural networks (MLP, convolutional neural networks, self-organizing maps).
11. Fundamentals of web application development: Databases, requests, API.
12.,13. Basics of user interface development.

https://github.com/CVUT-FS-12110/Python-for-Scientific-Computations-and-Control/tree/master
https://moodle-vyuka.cvut.cz/

Python, scientific computing, numerical simulations, linear algebra, object-oriented programming, data processing, data visualization, artificial intelligence