Practical Course: Intelligent Data Analysis for Computer Security (Datalab)

In this practical course, the students develop learning-based systems for different computer security tasks, thereby intensifying their knowledge gained in the lecture "Machine Learning for Computer Security." The students have the unique opportunity to design, implement, and evaluate systems based on real-world data used in computer security research. The "Datalab" is composed of 6 units with several individual tasks covering different topics from classical computer security research, such as attack detection, spam classification, or vulnerability discovery. In each unit, the students develop an approach, train and validate it on known data, and submit their solution to the course platform, where the approach is tested against unknown data. The best approaches are awarded at the end of the semester and presented at a joint colloquium and get-together. More information can be found at https://intellisec.de/teaching/datalab
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Zusammenfassung

In this practical course, the students develop learning-based
systems for different computer security tasks, thereby intensifying
their knowledge gained in the lecture "Machine Learning for Computer Security."

The students have the unique opportunity to design, implement, and
evaluate systems based on real-world data used in computer security
research.

The "Datalab" is composed of 6 units with several individual tasks
covering different topics from classical computer security research,
such as attack detection, spam classification, or vulnerability
discovery. In each unit, the students develop an approach, train and
validate it on known data, and submit their solution to
the course platform, where the approach is tested against
unknown data.

The best approaches are awarded at the end of the semester and
presented at a joint colloquium and get-together.

More information can be found at https://intellisec.de/teaching/datalab

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Deutsch
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Zugriff
1. Okt 2020, 12:00 - 31. Mär 2021, 23:55
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Bis: 9. Nov 2020, 23:55
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Spätester Kursaustritt
16. Nov 2020
Veranstaltungszeitraum
10. Nov 2020 - 16. Feb 2021

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1736690