Olga Fink: Katalogdaten im Herbstsemester 2021 |
| Name | Frau Dr. Olga Fink |
| Lehrgebiet | Intelligente Instandhaltungssysteme |
| Adresse | EPFL ENAC IIC IMOS Station 18 GC G1 494 (Bâtiment GC) 1015 Lausanne SWITZERLAND |
| Telefon | 0041216936629 |
| ofink@ethz.ch | |
| Departement | Bau, Umwelt und Geomatik |
| Beziehung | Dozentin |
| Nummer | Titel | ECTS | Umfang | Dozierende | |
|---|---|---|---|---|---|
| 101-0522-10L | Doctoral Seminar Data Science and Machine Learning in Civil, Env. and Geospatial Engineering  Findet dieses Semester nicht statt. Number of participants limited to 21. | 1 KP | 2S | B. Soja, E. Chatzi, F. Corman, O. Fink, I. Hajnsek, K. Schindler | |
| Kurzbeschreibung | 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. | ||||
| Lernziel | - 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 | ||||
| Inhalt | Current research at D-BAUG will be presented and discussed. | ||||
| Voraussetzungen / Besonderes | 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. | ||||
| 101-0523-12L | Frontiers in Machine Learning Applied to Civil, Env. and Geospatial Engineering (HS21) Number of participants limited to 21. | 1 KP | 2S | M. A. Kraus, E. Chatzi, F. Corman, O. Fink, I. Hajnsek, M. Lukovic, K. Schindler, B. Soja, B. Sudret, M. J. Van Strien | |
| Kurzbeschreibung | 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). | ||||
| Lernziel | 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. | ||||
| Inhalt | 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. | ||||
| Voraussetzungen / Besonderes | 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. | ||||
| 364-1058-00L | Risk Center Seminar Series | 0 KP | 2S | B. J. Bergmann, D. Basin, A. Bommier, D. N. Bresch, L.‑E. Cederman, P. Cheridito, F. Corman, O. Fink, H. Gersbach, C. Hölscher, K. Paterson, H. Schernberg, F. Schweitzer, D. Sornette, B. Stojadinovic, B. Sudret, J. Teichmann, U. A. Weidmann, S. Wiemer, M. Zeilinger, R. Zenklusen | |
| Kurzbeschreibung | This course is a mixture between a seminar primarily for PhD and postdoc students and a colloquium involving invited speakers. It consists of presentations and subsequent discussions in the area of modeling complex socio-economic systems and crises. Students and other guests are welcome. | ||||
| Lernziel | Participants should learn to get an overview of the state of the art in the field, to present it in a well understandable way to an interdisciplinary scientific audience, to develop novel mathematical models for open problems, to analyze them with computers, and to defend their results in response to critical questions. In essence, participants should improve their scientific skills and learn to work scientifically on an internationally competitive level. | ||||
| Inhalt | This course is a mixture between a seminar primarily for PhD and postdoc students and a colloquium involving invited speakers. It consists of presentations and subsequent discussions in the area of modeling complex socio-economic systems and crises. For details of the program see the webpage of the colloquium. Students and other guests are welcome. | ||||
| Skript | There is no script, but a short protocol of the sessions will be sent to all participants who have participated in a particular session. Transparencies of the presentations may be put on the course webpage. | ||||
| Literatur | Literature will be provided by the speakers in their respective presentations. | ||||
| Voraussetzungen / Besonderes | Participants should have relatively good mathematical skills and some experience of how scientific work is performed. | ||||