Computational Intelligence in Engineering
Key Info
Basic Information
- Degree:
- Master
- Semesters:
- Wintersemester
- Lecturer:
- Univ.-Prof. Dr.-Ing. Bernd Markert
- Language:
- English
Further Information
Contact
Content information
The elective course “Computational Intelligence in Engineering” is available for students enrolled in the engineering Master’s programs of RWTH Aachen University. It provides an overview of recent applications of computational intelligence and deep learning that are relevant to engineering. The first half of the course content is a theoretical introduction to the topic of machine learning in engineering and programming fundamentals in Python and Julia. In the second half of the course, the students apply their gained knowledge in project-based learning.
The course will be taught interactively, engaging the students using practical example projects.
The following topics are covered:
- Time-variant dynamic processes from simulations or experiments
- Data acquisition and pre-processing
- Machine learning algorithms and neural network models
- Advanced neural networks architectures
- Project-specific engineering problems
- Programming fundamentals in Python and Julia for data-driven procedures
Further information
Course Requirements
Programming experience is advantageous, preferably the language Python.
Course Objectives
The course curriculum consists of interactive seminar lectures accompanied by semester project works. During the seminar lectures, the students shall receive the required theoretical information and supervision to independently plan, advance, and complete the projects in small groups. In addition, the seminars offer the opportunity to discuss challenges and problems arising during the projects. Finally, the students shall present the achievements and results obtained within the student projects in the scope of the seminar lectures and the accompanying computer lab exercises.
Knowledge / Understanding
The students will understand
- current trends in computational intelligence and their theoretical foundation in the context of engineering applications
- the advantages of machine learning algorithms in engineering but also the limits of the methods and when better not to use them
Teaching and learning methods
- e-Learning Moodle
- PowerPoint
- Python, Julia
Recommended literature
Goodfellow, I., Bengio, Y., Courville, A., Deep Learning. MIT Press. 2016.