Physics-Informed Machine Learning
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
MW2450 is a semester module in English language at Master’s level which is offered in summer semester.
This Module is included in the following catalogues within the study programs in physics.
- Catalogue of non-physics elective courses
|Total workload||Contact hours||Credits (ECTS)|
|150 h||45 h||5 CP|
Content, Learning Outcome and Preconditions
- understand key concepts underlying various machine learning algorithms
- be capable of applying discussed methods to test problems
- be able to implement the algorithms and use them on real data
- be able to integrate physical constraints and invariances into machine learning methods
- be able to compare and evaluate different methods in terms of their area of application, advantages/disadvantages, limitations, etc.
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VO||2||Physics-Informed Machine Learning||Zavadlav, J.||
Thu, 08:00–10:00, MW 1050
|UE||1||Physics-Informed Machine Learning - Exercises||
Responsible/Coordination: Zavadlav, J.
Thu, 12:00–13:00, MW 1050
Learning and Teaching Methods
So the students learn for example to understand key concepts underlying various machine learning algorithms, to implement the algorithms and use them on real data as well as to compare and evaluate different methods in terms of their area of application, advantages/disadvantages, limitations, etc..
Description of exams and course work
There is a possibility to take the exam in the following semester.