The course provides an introduction to data mining and model-driven decision support systems. Concepts, methods and examples are provided with a focus on acquiring hands-on experience with widely used methods, libraries and software systems.
|Mittwoch||06.11.2019||08:30 - 13:00||LC.-1.022 Übungsraum|
|Mittwoch||13.11.2019||08:30 - 13:00||LC.-1.022 Übungsraum|
|Mittwoch||20.11.2019||08:30 - 13:00||TC.3.02|
|Mittwoch||27.11.2019||08:30 - 13:00||TC.3.02|
|Mittwoch||04.12.2019||08:30 - 13:00||LC.-1.022 Übungsraum|
|Mittwoch||11.12.2019||08:30 - 13:00||LC.-1.022 Übungsraum|
|Mittwoch||08.01.2020||08:30 - 13:00||LC.-1.022 Übungsraum|
|Mittwoch||15.01.2020||09:00 - 11:30||TC.5.15|
After completing the course, students will know how to handle a number of basic data mining methods and how to provide computer-aided decision support.
Continuous assessment courses (PI) requiring attendance according to the rule set of the Master’s program (80%).
Each session combines classic lectures with interactive examples, which provides students with an immediate hands-on experience of various methods. Students are required to prepare material for each lecture and complete homework exercises. Alongside the course, students will work in groups on a data mining project of their choice, for which separate coaching sessions will be offered and which will be presented to class for peer discussion at the end of the course.
Group Project: 30%
Final Exam: 40%
2: >=75% to <87.5%
3: >=62.5% to <75%
4: >=50% to <62.5%
5 (fail): < 50%
Autor/in: Ledolter, Johannes