Course detail

Machine Learning in Engineering Calculations

FSI-QAI Acad. year: 2025/2026 Summer semester

The course outlines possible ways of applying machine learning in the context of engineering calculations. Students will get to know the basic principles of machine learning and artificial intelligence, with an emphasis on Reinforcement learning. The course includes an introduction to the Python programming language and its use for implementing suitable libraries for linking dynamic computational models and artificial intelligence tools.

Language of instruction

Czech

Number of ECTS credits

2

Entry knowledge

Basic knowledge of physical and engineering principles, as well as basic knowledge of the Python programming language.

Rules for evaluation and completion of the course

The course-unit credit award is conditional on active participation in the exercises, where the activity within the sub-tasks is continuously checked.

Attendance in exercises is compulsory, participation is checked by the teacher. The form of replacement of missed lessons is solved individually with the lecturer or with the guarantor.

Aims

The aim of the subject is to familiarize students with the basic approaches of the application of machine learning, specifically Reinforcement Learning in engineering calculations.

The graduate of the course will gain basic knowledge of algorithms and data structures, useful for effective implementation of Reinforcement Learning algorithms through the Python programming language.

The graduate will also gain practical experience in tasks in the field of connecting machine learning and selected engineering calculations, which can serve as inspiration for further development in this area.

The study programmes with the given course

Programme N-ADI-P: Automotive and Material Handling Engineering, Master's, elective

Programme N-AAE-P: Advanced Automotiv Engineering, Master's, elective

Type of course unit

 

Computer-assisted exercise

26 hours, compulsory

Syllabus


  1. Division of machine learning/artificial intelligence

  2. Reinforcement learning (RL): definition, introduction to basic concepts

  3. Introduction to the Python programming language

  4. Supervised learning in the Python environment (learning from data)

  5. Example of an RL task in the Python environment (Gymnasium)

  6. Model-based and model-free RL

  7. FMU: Creation, usage

  8. Construction of a computational model (Adams/Chrono/NVIDIA Modulus/Mujoco)

  9. Definition of inputs and outputs for RL: observations, actions

  10. Selection/creation of an agent and its policy

  11. Definition of reward function

  12. Training: definition of parameters influencing the process

  13. Using a trained agent in a simulation