Python for Scientific Computations and Control

Course guarantor:

Lecturer:

Tutor:

Supervisor:

Department of Instrumentation and Control Engineering

Synopsis:

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.

Requirements:

Syllabus of lectures:

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.

Syllabus of tutorials:

Study Objective:

Study materials:

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

https://moodle-vyuka.cvut.cz/

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans: