Michael Anton Kraus: Katalogdaten im Herbstsemester 2021

NameHerr Dr. Michael Anton Kraus
Adresse
Professur f. Baustatik u. Konstr.
Stefano-Franscini-Platz 5
ETH Zürich HIL E 42.2, Frau Renate
8093 Zürich
SWITZERLAND
E-Mailkraus@ibk.baug.ethz.ch
DepartementBau, Umwelt und Geomatik
BeziehungDozent

NummerTitelECTSUmfangDozierende
101-0139-00LScientific Machine and Deep Learning for Design and Construction in Civil Engineering Belegung eingeschränkt - Details anzeigen 3 KP4GM. A. Kraus, D. Griego
KurzbeschreibungThis course will present methods of scientific machine and deep learning (ML / DL) for applications in design and construction in civil engineering. After providing proper background on ML and the scientific ML (SciML) track, several applications of SciML together with their computational implementation during the design and construction process of the built environment are examined.
LernzielThis course aims to provide graduate level introduction into Machine and especially scientific Machine Learning for applications in the design and construction phases of projects from civil engineering.

Upon completion of the course, the students will be able to:
1. understand main ML background theory and methods
2. assess a problem and apply ML and DL in a computational framework accordingly
3. Incorporating scientific domain knowledge in the SciML process
4. Define, Plan, Conduct and Present a SciML project
InhaltThe course will include theory and algorithms for SciML, programming assignments, as well as a final project assessment.

The topics to be covered are:
1. Fundamentals of Machine and Deep Learning (ML / DL)
2. Incorporation of Domain Knowledge into ML and DL
3. ML training, validation and testing pipelines for academic and research projects

A comprehensive series of computer/lab exercises and in-class demonstrations will take place, providing a "hands-on" feel for the course topics.
SkriptThe course script is composed by lecture slides, which are available online and will be continuously updated throughout the duration of the course.
LiteraturSuggested Reading:
Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong Mathematics for Machine Learning
K. Murphy. Machine Learning: a Probabilistic Perspective. MIT Press 2012
C. Bishop. Pattern Recognition and Machine Learning. Springer, 2007
S. Guido, A. Müller: Introduction to machine learning with python. O'Reilly Media, 2016
O. Martin: Bayesian analysis with python. Packt Publishing Ltd, 2016
Voraussetzungen / BesonderesFamiliarity with MATLAB and / or Python is advised.
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.