After successful completion of the course, students are able to:

• read, describe, present, and critically discuss theoretical aspects of machine learning algorithms as well as to perform literature research for related work;
• extract the key aspects and results of a research topic and organizing them for a presentation;
• create presentation slides and present their research topic to their peers;
• summarize the key content and results of a research topic in their own words in a scientific seminar paper;
• critically review scientific paperson theoretical aspects of machine learning; and
• participate actively in joint research discussions on open machine learning problems and to document the observations and progress of the discussion.

The seminar is on selected theoretical aspects of machine learning algorithms, such as their computational complexity and learning theoretic error bounds.

Students read original papers, perform literature research for related work and discuss their findiungs in their group. They create presentation slides and receive feedback. They write their seminar paper using LaTeX and learn how to use bibliography management tools. Two scientific reviews are written according to a template and guidelines. Research discussions take place in groups of several students and their advisors.

• seminar presentation (40%)
• seminar paper (40%)
• scientific reviews (20%)