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

• read, describe, present, and critically discuss theoretical aspects of machine learning algorithms;
• perform literature research for related work;
• extract the key aspects and results of a research topic and organise 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 papers on theoretical aspects of machine learning;
• participate actively in joint research discussions.

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 and perform literature research for related work. They create presentation slides and give talks, and receive formative feedback. They write their seminar paper using LaTeX and learn how to use bibliography management tools. Additionally, they write two scientific reviews for which they also receive formative feedback. Research discussions take place online/in-person with the students and their advisors.

3ects -> 75h
8h literature search and topic proposal
12h preparing and attending presentations
42h researching the state-of-the-art in the topic
13h writing final report

The final grade is made up of the quality of

• the presentations,
• the submitted written report (including the draft report), and
• the written scientific reviews.