Course detail

Machine Learning in Engineering Calculations

FSI-QAIAcad. year: 2025/2026

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

Mode of study

Not applicable.

Guarantor

Ing. Aleš Prokop, Ph.D.

Department

Institute of Automotive Engineering (ÚADI)

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.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

SANGHI, Nimish. Deep Reinforcement Learning with Python. 1st ed. Apress Berkeley, CA, c2021. ISBN 978-1-4842-6809-4. (EN)
SUTTON, S.; BARTO, A. Reinforcement Learning: An Introduction. 2nd ed. The MIT Press, c2018. ISBN 978-0-262-19398-6. (EN)

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme N-ADI-P Master's 1 year of study, summer semester, elective
    2 year of study, summer semester, elective
  • Programme N-AAE-P Master's 2 year of study, summer semester, elective
    1 year of study, summer semester, elective

Type of course unit

 

Computer-assisted exercise

26 hod., compulsory

Teacher / Lecturer

Ing. Aleš Prokop, Ph.D.

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