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.
Supervisor
Department
Learning outcomes of the course unit
Prerequisites
Basic knowledge of physical and engineering principles, as well as basic knowledge of the Python programming language.
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
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.
Language of instruction
Czech
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.
Specification of controlled education, way of implementation and compensation for absences
The study programmes with the given course
Programme N-ADI-P: Automotive and Material Handling Engineering, Master's
branch ---: no specialisation, 2 credits, elective
Programme N-AAE-P: Advanced Automotiv Engineering, Master's
branch ---: no specialisation, 2 credits, elective
Type of course unit
Computer-assisted exercise
26 hours, compulsory
Syllabus
- Division of machine learning/artificial intelligence
- Reinforcement learning (RL): definition, introduction to basic concepts
- Introduction to the Python programming language
- Supervised learning in the Python environment (learning from data)
- Example of an RL task in the Python environment (Gymnasium)
- Model-based and model-free RL
- FMU: Creation, usage
- Construction of a computational model (Adams/Chrono/NVIDIA Modulus/Mujoco)
- Definition of inputs and outputs for RL: observations, actions
- Selection/creation of an agent and its policy
- Definition of reward function
- Training: definition of parameters influencing the process
- Using a trained agent in a simulation