Maarten Jan Van Strien: Catalogue data in Autumn Semester 2022 |
| Name | Dr. Maarten Jan Van Strien |
| Name variants | Maarten J. van Strien Maarten Jan van Strien |
| Address | Inst. f. Raum- u. Landschaftsentw. ETH Zürich, HIL H 51.2 Stefano-Franscini-Platz 5 8093 Zürich SWITZERLAND |
| Telephone | +41 44 633 24 64 |
| vanstrien@ethz.ch | |
| Department | Civil, Environmental and Geomatic Engineering |
| Relationship | Lecturer |
| Number | Title | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 101-0522-10L | Doctoral Seminar Data Science and Machine Learning in Civil, Env. and Geospatial Engineering  Number of participants limited to 21. | 1 credit | 1S | M. J. Van Strien, E. Chatzi, F. Corman, I. Hajnsek, M. A. Kraus, M. Lukovic, V. Ntertimanis, K. Schindler, B. Soja | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Current 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Current research at D-BAUG will be presented and discussed. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | This 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 101-0523-13L | Frontiers in Machine Learning Applied to Civil, Env. and Geospatial Engineering (HS22) | 1 credit | 1G | M. J. Van Strien, E. Chatzi, F. Corman, I. Hajnsek, M. A. Kraus, M. Lukovic, V. Ntertimanis, K. Schindler, B. Soja | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | This 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 103-0378-00L | Introduction to the Programming Language R | 3 credits | 2G | M. J. Van Strien, A. Grêt-Regamey | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | R 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Objective | The 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | A 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Optional supplementary reading is the book: Venables, Smith & R Core Team (2021) An Introduction to R. This book can be downloaded for free from: https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | No prior knowledge of R or any other programming language is required for this course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Competencies |
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||