Computational Intelligence in EngineeringCopyright: IAM
- Univ.-Prof. Dr.-Ing. Bernd Markert
The elective course „Computational Intelligence in Engineering“ is available for students enrolled in the engineering Master programs of RWTH Aachen University. It provides an overview over recent applications of computational intelligence and deep learning that are relevant to engineering. The course will be taught interactively, engaging the students using practical example projects.
- Data acquisition and preprocessing
- Feature scaling
- Principal component analysis
- Self-organizing maps and K-means
- Analyzing static data
- Linear and logistic regression
- Support vector machines
- Fully-connected neural networks
- Analyzing time-variant data from inertial measurement units (e.g. in smartphones)
- Recurrent neural networks
- Analyzing spatial data from simulations
- Convolutional neural networks
Teaching and learning methods
The course curriculum consists of interactive seminar lectures accompanied by two semester projects. During the seminar lectures, the students will receive the necessary 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 course of the projects. Finally, the achievements and results obtained within the student projects will be presented by the students in the scope of the seminars and the accompanying computer lab exercises.
Goodfellow, I., Bengio, Y., Courville, A., 2016. Deep Learning. MIT Press.
Schölkopf, B., Smola, A.J., Scholkopf, M.D. of the M.P.I. for B.C. in T.G.P.B., Bach, F., 2002. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press.
Keller, J.M., Liu, D., Fogel, D.B. , 2016. Fundamentals of Computational Intelligence. IEEE Press, Wiley.