Cyber Security Master |
Minor |
Information Systems |
Core Courses |
Number | Title | Type | ECTS | Hours | Lecturers |
---|
252-0535-00L | Advanced Machine Learning | W | 10 credits | 3V + 2U + 4A | |
252-0535-00 V | Advanced Machine Learning
The lectures will mostly be given in a lecture hall with limited attendance (at most 50% of lecture hall capacity). It will be possible to join remotely via zoom with acccess to slides, whiteboard, and speaker camera. Students can interact, e.g. ask questions, physically as well as digitally. The lectures will be recorded via zoom’s recording functionality. | | | 3 hrs | | J. M. Buhmann,
C. Cotrini Jimenez |
252-0535-00 U | Advanced Machine Learning | | | 2 hrs | | J. M. Buhmann,
C. Cotrini Jimenez |
252-0535-00 A | Advanced Machine Learning
Project Work, no fixed presence required. | | | 4 hrs | | J. M. Buhmann,
C. Cotrini Jimenez |
263-3010-00L | Big Data | W | 10 credits | 3V + 2U + 4A | |
263-3010-00 V | Big Data
The lecturers will communicate the exact lesson times of ONLINE courses. | | | 3 hrs | | G. Fourny |
263-3010-00 U | Big Data
Groups are selected in myStudies. The lecturers will communicate the exact lesson times of ONLINE courses. | | | 2 hrs | | G. Fourny |
263-3010-00 A | Big Data
Individual work to get hands-on experience with the technologies covered, no fixed presence required. | | | 4 hrs | | G. Fourny |
263-3845-00L | Data Management Systems | W | 8 credits | 3V + 1U + 3A | |
263-3845-00 V | Data Management Systems
"Hybrid" | | | 3 hrs | | G. Alonso |
263-3845-00 U | Data Management Systems | | | 1 hrs | | G. Alonso |
263-3845-00 A | Data Management Systems | | | 3 hrs | | G. Alonso |
|
Elective Courses |
Number | Title | Type | ECTS | Hours | Lecturers |
---|
263-2400-00L | Reliable and Interpretable Artificial Intelligence | W | 6 credits | 2V + 2U + 1A | |
263-2400-00 V | Reliable and Interpretable Artificial Intelligence
The lecturers will communicate the exact lesson times of ONLINE courses. | | | 2 hrs | | M. Vechev |
263-2400-00 U | Reliable and Interpretable Artificial Intelligence
Exercise session will start in the second week of the semester. | | | 2 hrs | | M. Vechev |
263-2400-00 A | Reliable and Interpretable Artificial Intelligence | | | 1 hrs | | M. Vechev |
263-3210-00L | Deep Learning | W | 8 credits | 3V + 2U + 2A | |
263-3210-00 V | Deep Learning
The lecturers will communicate the exact lesson times from «online» courses. | | | 3 hrs | | T. Hofmann |
263-3210-00 U | Deep Learning | | | 2 hrs | | T. Hofmann |
263-3210-00 A | Deep Learning | | | 2 hrs | | T. Hofmann |
263-5210-00L | Probabilistic Artificial Intelligence | W | 8 credits | 3V + 2U + 2A | |
263-5210-00 V | Probabilistic Artificial Intelligence
The lectures will mostly be given in a lecture hall with limited attendance (at most 50% of lecture hall capacity). It will be possible to join remotely via zoom with acccess to slides, whiteboard, and speaker camera. Students can interact, e.g. ask questions, physically as well as digitally. The lectures will be recorded via zoom’s recording functionality. | | | 3 hrs | | A. Krause |
263-5210-00 U | Probabilistic Artificial Intelligence | | | 2 hrs | | A. Krause |
263-5210-00 A | Probabilistic Artificial Intelligence | | | 2 hrs | | A. Krause |