Machine Learning in Civil Engineering

  Buildings, road and vehicles, connected via neural network Copyright: © ISAC  

Course Language: English

The course "Machine Learning in Civil Engineering" is relevant for these courses of study:

More detailed information can be found in the respective Examination Regulations in the official announcements of RWTH Aachen University.


Course Description

The module imparts knowledge in the following subject areas:

  • Introduction Programming
  • Classification and regression of traffic data with Supervised Learning
  • Clustering with Unsupervised Learning
  • Basics of Neural Networks
  • Application of larger Neural Networks

A lecture covers the mathematical and theoretical basics of different machine learning methods. In addition, the lecture is accompanied by a practical exercise in which students work independently on self-programming exercises and develop routines in program code relevant to the Learning Outcomes. Students complete a group project in which they identify, analyze, and test data sets. The theoretical basics are tested beyond the project work in the context of an online exam.

Learning Objectives

Students learn basic concepts and ideas of machine learning in this module. They deal with different learning algorithms, understand the respective advantages and disadvantages and gain an intuition which algorithms can be applied to which problems. Students are then able to apply the learned algorithms in a suitable programming language (Python) to analyze them for new, large data sets.


Course Scope

  • Lecture: 2 SWS
  • Seminar: 2 SWS


Univ.-Prof. Dr.-Ing. Jörg Blankenbach, Geodetic Institute and Chair of Construction Informatics & Geoinformation Systems (GIA RWTH)

Dr.-Ing. Adrian Fazekas (ISAC)

Scientific Staff

Tom Schumann

Mohamed Kastouri

Arnd Pettirsch