Maarten Jan Van Strien: Katalogdaten im Herbstsemester 2021

NameHerr Dr. Maarten Jan Van Strien
NamensvariantenMaarten J. van Strien
Maarten Jan van Strien
Inst. f. Raum- u. Landschaftsentw.
ETH Zürich, HIL H 51.2
Stefano-Franscini-Platz 5
8093 Zürich
Telefon+41 44 633 24 64
DepartementBau, Umwelt und Geomatik

101-0523-12LFrontiers in Machine Learning Applied to Civil, Env. and Geospatial Engineering (HS21) Belegung eingeschränkt - Details anzeigen
Number of participants limited to 21.
1 KP2SM. A. Kraus, E. Chatzi, F. Corman, O. Fink, I. Hajnsek, M. Lukovic, K. Schindler, B. Soja, B. Sudret, M. J. Van Strien
KurzbeschreibungThis 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).
LernzielStudents 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.
InhaltWith 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.
Voraussetzungen / BesonderesThis 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.
103-0378-00LIntroduction to the Programming Language R Belegung eingeschränkt - Details anzeigen 3 KP2GM. J. Van Strien, A. Grêt-Regamey
KurzbeschreibungR is one of the most popular programming language in science and practice for data analysis, modelling and visualisation. In this course, you will learn the basics of R and some common applications of R, such as making plots, regression analysis and working with spatial data. The weekly computer labs start with a short lecture followed by exercises that have to be handed in to pass the course.
LernzielThe overall objective of this course is to provide an introduction to the programming language R and to build confidence to apply R in other courses. More specifically, the objectives are:
- Understand how to import and export data, and how to work with the most important types of R-objects (e.g. vectors, data frames, matrices and lists).
- Learn how to create meaningful and visually attractive graphics and apply this knowledge to several datasets.
- Learn how to apply several types of important functions (e.g. for- and while-loops, if-else statements, data manipulation).
- Understand descriptive statistics and regression analysis and apply this knowledge to analyse several datasets.
- Understand the possibilities of analysing and plotting spatial data.
- Learn how to write own functions.
InhaltThe course has a strong focus on “learning by doing”. During the weekly computer lab sessions, students will be given an introduction to the programming language R. Each lab session will start with a short introductory lecture, after which students work through the script and complete the exercises. During the lab sessions, the lecturers will be available to answer individual questions. The main topics that will be covered in the lab sessions are:
- importing and exporting data
- types of R-objects
- data scraping
- plotting data
- descriptive statistics
- data manipulation
- conditionals and loops
- regression analysis
- plotting and analysing spatial data
- writing own functions

In the 7th and 14th week of the course, students have the time to finish the exercises that should be handed in at the end of those weeks.
SkriptA script with theory, examples and exercises will be handed out at the beginning of the course. Data for the exercises will be made available via Moodle.
LiteraturOptional supplementary reading is the book: Venables, Smith & R Core Team (2021) An Introduction to R. This book can be downloaded for free from:
Voraussetzungen / BesonderesNo prior knowledge of R or any other programming language is required for this course.
Fachspezifische KompetenzenKonzepte und Theoriengefördert
Verfahren und Technologiengeprüft
Methodenspezifische KompetenzenAnalytische Kompetenzengeprüft
Medien und digitale Technologiengefördert
Soziale KompetenzenKommunikationgefördert
Kooperation und Teamarbeitgefördert
Menschenführung und Verantwortunggefördert
Selbstdarstellung und soziale Einflussnahmegefördert
Sensibilität für Vielfalt gefördert
Persönliche KompetenzenAnpassung und Flexibilitätgefördert
Kreatives Denkengeprüft
Kritisches Denkengeprüft
Integrität und Arbeitsethikgeprüft
Selbstbewusstsein und Selbstreflexion gefördert
Selbststeuerung und Selbstmanagement gefördert