101-0139-00L  Scientific Machine and Deep Learning for Design and Construction in Civil Engineering

SemesterAutumn Semester 2021
LecturersM. A. Kraus, D. Griego
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
101-0139-00 GScientific Machine and Deep Learning for Design and Construction in Civil Engineering
14-16 theory
16-18 group work
4 hrs
Mon13:45-17:30HPK D 3 »
M. A. Kraus, D. Griego

Catalogue data

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.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits3 credits
ExaminersM. A. Kraus, D. Griego
Typeend-of-semester examination
Language of examinationEnglish
RepetitionA repetition date will be offered in the first two weeks of the semester immediately consecutive.
Mode of examinationoral 5 minutes
Additional information on mode of examinationMandatory 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 (5 min). (50% of the final grade).

Learning materials

No public learning materials available.
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

Places20 at the most
Waiting listuntil 01.10.2021

Offered in

ProgrammeSectionType
Civil Engineering MasterDigitalisation Specific CoursesWInformation
Civil Engineering MasterProject Based CoursesWInformation
Civil Engineering MasterMajor in Structural EngineeringWInformation
Doctoral Department of ArchitectureDoctoral and Post-Doctoral CoursesW+Information
Doctoral Department of Civil, Environmental and Geomatic EngineeringAdditional CoursesWInformation
Integrated Building Systems MasterSpecialised CoursesWInformation