Oral Presentation (about 30 minutes) and written elaboration (approx. 10 pages), leading the discussion once

In this seminar, machine learning approaches with applications to bioinformatics
will be discussed. These can be, among others, the following: supervised
classification; deep learning in bioinformatics, support vector machines
for classification; dimension reduction methods; probabilistic graphical models;
applications to the fields of genomics, transcriptomics, evolution, systems biology,
text mining and other topics supplemented by current research will be
discussed.

The students can independently work with supervision on a challenging topic
through systematic research. They summarize, assess, classify, scientifically correctly
represent and present concepts and methods of machine learning that
are applied to bioinformatics problems. On the one hand, students will get an
overview of modern knowledge in the field of machine learning and their importance
for various questions in bioinformatics. On the other hand, students
will know that there are still many open research questions in this field. By
studying current articles, the students have not only improved their reading
and learning skills, but also their personal responsibility. The form of learning
used in the seminar is intended to help the students to develop self-confidence
(presentation) and the ability to criticise and communicate (subsequent discussion).

Articles / scientific publications for each individual topic