101-0523-14L  Frontiers in Machine Learning Applied to Civil, Env. and Geospatial Engineering

SemesterAutumn Semester 2023
LecturersV. Ntertimanis, E. Chatzi, F. Corman, I. Hajnsek, M. A. Kraus, M. Lukovic, K. Schindler, B. Soja, M. J. Van Strien
Periodicitynon-recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
101-0523-14 GFrontiers in Machine Learning Applied to Civil, Env. and Geospatial Engineering
Remark: Each autumn semester (HS) a new course with a new course number.
1 hrs
Wed/2w13:45-15:30HIL E 10.1 »
V. Ntertimanis, E. Chatzi, F. Corman, I. Hajnsek, M. A. Kraus, M. Lukovic, K. Schindler, B. Soja, M. J. Van Strien

Catalogue data

AbstractThis 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).
ObjectiveStudents 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.
ContentWith 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.
Prerequisites / NoticeThis doctoral seminar is intended for doctoral students affiliated with the Department of Civil, Environmental and Geomatic Engineering. Other students who work on related topics need approval by at least one of the organisers to register for the seminar.

Participants are expected to possess elementary skills in statistics, data science and machine learning, including both theory and practical modelling and implementation. The seminar targets students who are actively working on related research projects.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits1 credit
ExaminersV. Ntertimanis, E. Chatzi, F. Corman, I. Hajnsek, M. A. Kraus, M. Lukovic, K. Schindler, B. Soja, M. J. Van Strien
Typeungraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.

Learning materials

No public learning materials available.
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

There are no additional restrictions for the registration.

Offered in

ProgrammeSectionType
Doctorate Civil, Environmental and Geomatic EngineeringSubject SpecialisationWInformation