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Frontiers in Machine Learning Applied to Civil, Env. and Geospatial Engineering (FS21)
Last Updated: 2026-02-05 15:55:11
Abstract
This doctoral seminar organised by the D-BAUG platform on data science and machine learning aims at discussing recent research papers in the field of machine learning and analyzing the transferability/adaptability of the proposed approaches to applications in the field of civil and environmental engineering (if possible and applicable, also implementing the adapted algorithms).
Objective
Students will • Critically read scientific papers on the recent developments in machine learning • Put the research in context • Present the contributions • Discuss the validity of the scientific approach • Evaluate the underlying assumptions • Evaluate the transferability/adpatability of the proposed approaches to own research • (Optionally) implement the proposed approaches.
Content
With the increasing amount of data collected in various domains, the importance of data science in many disciplines, such as infrastructure monitoring and management, transportation, spatial planning, structural and environmental engineering, has been increasing. The field is constantly developing further with numerous advances, extensions and modifications. The course aims at discussing recent research papers in the field of machine learning and analyzing the transferability/adaptability of the proposed approaches to applications in the field of civil and environmental engineering (if possible and applicable, also implementing the adapted algorithms). Each student will select a paper that is relevant for his/her research and present its content in the seminar, putting it into context, analyzing the assumptions, the transferability and generalizability of the proposed approaches. The students will also link the research content of the selected paper to the own research, evaluating the potential of transferring or adapting it. If possible and applicable, the students will also implement the adapted algorithms The students will work in groups of three students, where each of the three students will be reading each other’s selected papers and providing feedback to each other.
General Information
- Language
- English
- Levels
- DR
Examination
- Type
- ungraded semester performance
Registration & Places
- Max Places
- 21
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| seminar |
Frontiers in Machine Learning Applied to Civil, Env. and Geospatial Engineering
The lecturers will communicate the exact lesson times of ONLINE courses.
|
|
2 h weekly |
Offered In
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Doctoral Department of Civil, Environmental and Geomatic Engineering (More Information at: )
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