Machine Learning Workflows for Digital Energy Systems

The course covers the following:

- statistical Learning (supervised/unsupervised),

- supervised regression,

- neural Networks,

- designing machine learning workflows

- physics-informed learning for energy applications

- learning with inductive bias in grids

Written exam (100% of grade). Passing the homeworks and project is a prerequisite to be eligible for taking the written exam.

In case of an insufficient final result, repair options may exist in accordance with Article 17A, Times and number of examinations, sub 1, of the Teaching and Examination Regulations.The repair option is to resubmit the individual project or homework within 10 days after announcing the homework or project pass/fail grade.

Disclaimer: information may change depending on unforeseen circumstances or measures (see: TER Art 2, sub 5).