Instructors: Dr. Cornelius Andreas Stefan Weber
Event type:
Practical course/lab
Displayed in timetable as:
DAIS-Üb
Hours per week:
2
Language of instruction:
German/English
Min. | Max. participants:
- | 100
More information:
Dear Students, due to the coronavirus the seminar will NOT be held on campus; please
ignore the given room numbers.
Seminar material will be provided online. Please check this STiNE page after Monday 20.
April for further announcements.
Comments/contents:
The practical course complements the lecture “Data-driven Intelligent Systems”. The students will work out concepts and algorithms presented in the lecture using code examples in a self-contained manner. In addition, the interconnection between the theory and application will be discussed within groups as well as with the tutor.
Learning objectives:
The students work out current topics in the area of Data-driven Intelligent Systems. They learn to analyse complex interrelations between theory and practical applications, and will be motivated to transfer the gained knowledge to the exercises. Moreover, working in pairs as well as presentation of joint work and discussions with the tutor deepens soft skills such as teamwork and communication.
Didactic concept:
The practical exercises consist of classwork, which will be received and worked on during the class in groups of two students, using the pool computers. Solutions will be presented and discussed by the students in plenary during the practical course. Classes are scheduled every two weeks. Successful completion of the course assumes attendance at the lectures, active participation at the exercises, and regular preparation at home.
Language:
We will offer the lecture in English to provide an opportunity to become acquainted with the standard language of science and engineering. We will offer the complementing practical courses in English as well as in German to adapt to your preferences. Also we will support you, both for the topic and the language, as good as we can. German discussions are welcome at any time.
Literature:
- Han J.; Kamber, M. Data Mining: Concepts and Techniques. Elsevier; Morgan Kaufmann, Amsterdam, 2006.
- Kantardzic, M. Data Mining. Wiley, 2011.
- Marsland, S. Machine Learning - An Algorithmic Perspective. CRC Press, 2015.
Software: Python
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