Zoom-Link zu den Online-Vorträgen, öffnet eine externe URL in einem neuen Fenster

Abstract:

The talk covers several typical challenges from “Data Science” arising in research
projects at the Software Competence Center Hagenberg (SCCH). Classical statistics
as well as modern complex machine learning methods, such as neural networks, are
applied to real-world use cases from industry.
In the first part, a short presentation of SCCH as an institution for applied research
is given, which is particularly interesting for students with an interest in a master or
PhD thesis on practical problems.
The second part is a summary of various projects involving real-world data with a
focus on recurring statistical problems from manufacturing scenarios. In particular,
methods related to anomaly detection, diagnosis and prediction using machine
learning methods are discussed with some care given to the black-box stigma of typical
modern machine learning methods. The presentation is intended to identify classical
methods and open research questions from statistics relevant for approaches taken by
SCCH’s strategy on predictive maintenance.

*SCCH – Software Competence Center Hagenberg
**FAW - Institute for Application-oriented Knowledge Processing der JKU