// Goals of the course ///
At the end of the course, students will be able to
* analyze the technical merits of specific developments regarding the topic of machine learning in the field of IT-security,
* substantiate their analyses using existing and scientific documented knowledge,
* clearly write up those analyses in a concise scientific report, and
* further develop an attitude in which being able to clearly explain matters is geared to optimize the quality of feedback.

/// Course contents ///
The course contents consist of studying and assessing selected methods in machine learning in an IT-security context. Each available topic is to be tackled by an individual student. For this purpose students will be provided with material such as scientific articles to help them understand the topic at hand. Part of the course consists of discovering additional material. Students will dig deep into the selected topic. Finally, students will present their analyses and findings in two ways: in a concise scientific report as well as in a 20 min. presentation, which is followed by a discussion and a round of feedback. At the beginning of the course, all available topics will be introduced to the students so that they can pick a topic suitable for them.

/// Assessment ///
Students will be assessed on the basis of their written scientific report (high weight), their presentation and consequent discussion (medium to high weight), and the general process (low weight; includes: independence, planning, active involvement, …)

/// Topics ///
More details on the topics will follow.