Students are able to carry out a non-trivial project in the area of data analysis or machine learning, in a self-responsible and independent manner.
Skills
Knowledge
Students acquire experience in
reading the data science or machine learning literature (k2);
implementing state-of-the-art data analysis algorithms (k3);
designing and carrying out experiments (k6),
and documenting and critically evaluating the results (k4).
The knowledge comes from the individual courses of the chosen major subject.
Criteria for evaluation
The evaluation criteria are specified by the teacher at the beginning of the semester. Usually the course is evaluated by continuous project monitoring as well as by a final presentation and possibly a final written report.
Methods
Joint discussion of project target; intensive discussion meetings at irregular intervals; joint identification of pertinent literature.
