Introduction to Scientific Machine Learning for Engineers
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.
MW2435 is a semester module in English language at Master’s level which is offered in winter semester.
This Module is included in the following catalogues within the study programs in physics.
- Focus Area Imaging in M.Sc. Biomedical Engineering and Medical Physics
|Total workload||Contact hours||Credits (ECTS)|
|90 h||45 h||3 CP|
Content, Learning Outcome and Preconditions
- understand Kernel Methods.
- comprehend the construct of neural networks and training via backpropagation.
- understand which neural network class is best suited for a specific application.
- develop the ability to construct a machine learning workflow from basic building blocks.
- Basics of Probability Theory
- Foundational Understanding of Programming
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VO||2||Introduction to Scientific Machine Learning for Engineers (MW2435)||Adams, N. Pähler, L.||
|UE||1||Exercises on Introduction to Scientific Machine Learning for Engineers (MW2435)||Adams, N. Pähler, L.||
Thu, 11:00–12:30, virtuell
Learning and Teaching Methods
Through instruction and self-learning the students learn the construction behind neural networks, the up- and downsides of training via backpropagation and a problem-specific grasp for the suitability of neural network classes. Thus, their foundational knowledge is gained to subsequently construct a machine learning workflow from basic building blocks.
- Pattern Recognition and Machine Learning, by Christopher M. Bishop
- Machine Learning: A Probabilistic Perspective, by Kevin P. Murphy
Description of exams and course work
There is a possibility to take the exam in the following semester.