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Maschinelles Lernen

A diagram presenting how to create a machine learning classifier to identify low quality ensemble members

Practical machine learning for engineers

This project aims at teaching students with an engineering background how to apply machine learning. These students already master all the prerequisites required to investigate data through a machine learning lens. The goal of this project is to offer them an additional interdisciplinary training. As a result, the students should be capable of employing the most effective machine learning techniques in the industry.

To achieve this goal, it is required to adapt the existing machine learning courses at the Ruhr University to the needs and background of the students from the engineering field. Hereby, it is proposed to divide the training into two phases. First, machine learning basics will be integrated into lectures. Existing lectures for students studying Applied Informatics will therefore be modified and made available for engineering students. In addition, case study projects will allow the students to apply machine learning techniques in real world problems.

The project covers three areas of responsibility: a) the supervision of additional lecture-accompanying exercises, which are specially tailored to engineers, b) the design and technical support of practical case study projects, and c) the creation of new learning content in order to adapt the existing lectures.

Project Data

Title: Practical machine learning for engineers

Project Partner:

Institut für Neuroinformatik

Ruhr-University Bochum

Contact:
Chair of Computing in Engineering
Bldg. IC, Room 6-59
Universitätsstraße 150
44780 Bochum