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101-0139-00L 3 Credits DR , MSC D-MATL , D-BAUG , D-ARCH
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Scientific Machine and Deep Learning for Design and Construction

Lecturers & Examiners: Prof. Dr. Bernd Bickel, Dr. Andreas Müller
VVZ CR n/a

Last Updated: 2026-06-01 11:30:37

Abstract

This course will present methods of scientific machine and deep learning (ML / DL) for applications in design and construction. 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.

Objective

This course aims to provide a graduate-level introduction to machine learning, with a particular focus on scientific machine learning for applications in the design and construction phases of projects from architecture and 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. Incorporate scientific domain knowledge in the SciML process. 4. Define, plan, conduct and present a SciML project.

Content

The 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. Generative AI and its applications. 4. 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.

Resources

Lecture Notes

The course script is composed by lecture slides, which are available online and will be continuously updated throughout the duration of the course.

Literature

Suggested 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

General Information

Language
English
Levels
DR , MSC
Frequency
Yearly recurring

Examination

Type
end-of-semester examination
Mode
oral 10 minutes
Mandatory final project examination. The final grade will be obtained based on:1. Project presentation (15 min) and public Q&A (5 min). Projects are conducted in pairs (50% of the final grade). This compulsory continuous performance assessment task need not be passed on its own; it is awarded a grade which counts proportionally towards the total course unit grade.2. Followed by a non-public individual oral examination (10 min) (50% of the final grade).

Registration & Places

Max Places
20

Course Components

Type Title Time & Place Hours
lecture with exercise Scientific Machine and Deep Learning for Design and Construction
14-16 theory 16-18 group work
  • Mon 13:45-17:30 (HCI E 8)
4 h weekly

Offered In