Maarten Jan Van Strien: Catalogue data in Spring Semester 2021

Name Dr. Maarten Jan Van Strien
Name variantsMaarten J. van Strien
Maarten Jan van Strien
Address
Inst. f. Raum- u. Landschaftsentw.
ETH Zürich, HIL H 42.3
Stefano-Franscini-Platz 5
8093 Zürich
SWITZERLAND
Telephone+41 44 633 24 64
E-mailvanstrien@ethz.ch
DepartmentCivil, Environmental and Geomatic Engineering
RelationshipLecturer

NumberTitleECTSHoursLecturers
101-0522-10LDoctoral Seminar Data Science and Machine Learning in Civil, Env. and Geospatial Engineering Restricted registration - show details
Number of participants limited to 21.
1 credit2SB. Soja, E. Chatzi, F. Corman, O. Fink, I. Hajnsek, M. A. Kraus, M. Lukovic, K. Schindler, M. J. Van Strien
AbstractCurrent research in machine learning and data science within the research fields of the department. The goal is to learn about current research projects at our department, to strengthen our expertise and collaboration with respect to data-driven models and methods, to provide a platform where research challenges can be discussed, and also to practice scientific presentations.
Objective- learn about discipline-specific methods and applications of data science in neighbouring fields
- network people and methodological expertise across disciplines
- establish links and discuss connections, common challenges and disciplinespecific differences
- practice presentation and discussion of technical content to a broader, less specialised scientific audience
ContentCurrent research at D-BAUG will be presented and discussed.
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.
101-0523-11LFrontiers in Machine Learning Applied to Civil, Env. and Geospatial Engineering (FS21) Restricted registration - show details
Number of participants limited to 21.
1 credit2SM. Lukovic, E. Chatzi, F. Corman, O. Fink, I. Hajnsek, M. A. Kraus, K. Schindler, B. Soja, M. J. Van Strien
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.