101-0139-00L Scientific Machine and Deep Learning for Design and Construction in Civil Engineering
| Semester | Autumn Semester 2023 |
| Lecturers | M. A. Kraus, D. Griego |
| Periodicity | yearly recurring course |
| Language of instruction | English |
Courses
| Number | Title | Hours | Lecturers | ||||
|---|---|---|---|---|---|---|---|
| 101-0139-00 G | Scientific Machine and Deep Learning for Design and Construction in Civil Engineering 14-16 theory 16-18 group work | 4 hrs |
| M. A. Kraus, D. Griego |
Catalogue data
| Abstract | This 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. |
| Learning objective | This 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 |
| 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. 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 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 |
| Prerequisites / Notice | Familiarity 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 credits | 3 credits |
| Examiners | M. A. Kraus, D. Griego |
| Type | end-of-semester examination |
| Language of examination | English |
| Repetition | The performance assessment is only offered at the end after the course unit. Repetition only possible after re-enrolling. |
| Mode of examination | oral 5 minutes |
| Additional information on mode of examination | 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 (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
| Places | 20 at the most |
| Waiting list | until 29.09.2023 |
