Danielle Griego: Catalogue data in Autumn Semester 2021

Name Dr. Danielle Griego
Address
ETH+-Initiative Design++
ETH Zürich, HIF E 20.1
Laura-Hezner-Weg 7
8093 Zürich
SWITZERLAND
E-mailgriego@arch.ethz.ch
DepartmentCivil, Environmental and Geomatic Engineering
RelationshipLecturer

NumberTitleECTSHoursLecturers
101-0139-00LScientific Machine and Deep Learning for Design and Construction in Civil Engineering Restricted registration - show details 3 credits4GM. A. Kraus, D. Griego
AbstractThis 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.
ObjectiveThis 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
ContentThe 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.
Lecture notesThe course script is composed by lecture slides, which are available online and will be continuously updated throughout the duration of the course.
LiteratureSuggested 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
Prerequisites / NoticeFamiliarity with MATLAB and / or Python is advised.