Benedikt Soja: Catalogue data in Autumn Semester 2020 |
| Name | Prof. Dr. Benedikt Soja |
| Field | Space Geodesy |
| benedikt.soja@geod.baug.ethz.ch | |
| URL | http://twitter.com/b_soja |
| Department | Civil, Environmental and Geomatic Engineering |
| Relationship | Assistant Professor (Tenure Track) |
| 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 | 2S | K. Schindler, E. Chatzi, F. Corman, O. Fink, I. Hajnsek, M. A. Kraus, M. Lukovic, M. Raubal, B. Soja, B. Sudret | |
| 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. | ||||
| Learning 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. | ||||
| 101-0523-10L | Frontiers in Machine Learning Applied to Civil, Env. and Geospatial Engineering Number of participants limited to 21. | 1 credit | 2S | O. Fink, E. Chatzi, F. Corman, I. Hajnsek, M. A. Kraus, M. Lukovic, M. Raubal, K. Schindler, B. Soja, B. Sudret | |
| 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). | ||||
| Learning 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. | ||||
| 103-0627-00L | Space Geodesy Lab | 5 credits | 3P | G. Möller, R. Hohensinn, M. Rothacher, B. Soja | |
| Abstract | Space Geodesy Lab allows you to deepen your knowledge about space-geodetic techniques, in particular of GNSS, VLBI, SLR, satellite altimetry and gravity missions for monitoring the environment and changes within the Earth system. | ||||
| Learning objective | Students enrolled in this course will be given the possibility to learn about space-geodetic methods to solve a specific research problem. As a result, you will become familiar with the entire processing chain from gathering of raw measurements to geodetic products like reference frames, station motions, Earth orientation parameters, atmospheric and climate variables, or the Earth gravity field and its variations. | ||||
| Content | For a small project based on space geodetic measurements and methods (or a related project of your choice), you or a group of 2-3 students will be provided with the necessary equipment, access to data and analysis tools for solving your research question. Therefore, we expect autonomous development, planning, data analysis and interpretation of the results. At the end of the semester you will be ask to present your findings and to submit a report summarizing your semester activities. As needed, further background will be given during the semester. | ||||
| Lecture notes | div. sources | ||||
| Literature | M. Rothacher – Space Geodesy lecture notes, additional literature will be distributed during lectures | ||||
| Prerequisites / Notice | Basic knowledge about satellite geodesy, reference frames and the Earth gravity field. Programming skills in Matlab, Python or similar. | ||||
| 103-0657-01L | Signal Processing, Modeling, Inversion | 3 credits | 2G | B. Soja | |
| Abstract | Topics related to time series analysis, modeling, parameter estimation, prediction, and interpretation. Theoretical concepts will be applied to geodetic problems. | ||||
| Learning objective | Students have various methods at hand to mathematically formulate specific scientific problems. They are able to analyse observational data, estimate numerical and analytical models, and predict parameters into the future. The students can evaluate and interpret measurements and models derived from them. They know the necessary terminology in order to study expert literature. | ||||
| Content | Topics covered in this lecture include: time series analysis, Fourier transformation, stochastic processes, ARMA, analytical and numerical modeling, model selection, linear and non-linear parameter estimation, sequential parameter estimation and filtering, machine learning for time series analysis and prediction, interpretation of measurements and derived results. The theoretical concepts will be illustrated by concrete examples commonly found in geodetic applications. | ||||
| Lecture notes | Lecture slides and notes | ||||
| Literature | Script Alain Geiger: Geoprocessing Additional literature will be referred to in class | ||||
| Prerequisites / Notice | Courses corresponding to: Analysis I+II, Linear Algebra I, Parameter Estimation | ||||