Computational Intelligence in Engineering

  Gear with a schematic representation of a brain with a neuronal network Copyright: © IAM

Key Info

Basic Information

Degree:
Master
Semesters:
Wintersemester
Lecturer:
Univ.-Prof. Dr.-Ing. Bernd Markert
Language:
English

Further Information

Contact

Bernd Markert

Name

Bernd Markert

Institutsleiter, Rektoratsbeauftragter für Alumni

Phone

work
+49 241 80 94600

Email

E-Mail
 

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.

Topics

  • 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
 

Further information

Course Requirements

Course Objectives

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.

Recommended literature

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.