Data Mining II - Advanced Topics in Data Mining
News:
Announcement: Course materials for the semester will be distributed over Moodle. Please follow this link to see the content.
Timetable
Day | Time | Frequency | Period | Room | Lecturer | Remarks | Max. participants |
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Vorlesung(V) - Lecture - Dates/Times/Location: | |||||||
Mon. | 15:00 bis 17:00 | weekly |
09.10.2023 to 22.01.2024 | G29-307 (Verwaltung durch FIN) | Spiliopoulou | 60 | |
Übung (Ü) - Exercise - Dates/Times/Location: Group 1 | |||||||
Thu. | 15:00 bis 17:00 | weekly | 12.10.2023 to 25.01.2024 | G22A-211 (40 Pl.) | Unnikrishnan | Bitte beachten / please note: Registration/Votierung | 20 |
Übung (Ü) - Exercise - Dates/Times/Location: Group 2 | |||||||
Tue. | 11:00 bis 13:00 | weekly | 10.10.2023 to 23.01.2024 | G22A-209 (40 Pl.) | Jamaludeen | Bitte beachten / please note: Registration/Votierung | 20 |
Overview (from LSF)
Learning Content | In this course, we discuss model learning and model adaption on dynamic data.
WHAT IS DIFFERENT FROM CONVENTIONAL MODEL LEARNING? When a model is inferred, it reflects the characteristics of the population it was built upon. But the population - be it user preferences in the web, be patients arriving in a hospital clinic, be it energy consumption of a town, be it the work accomplished by a machine in an industrial floor - this population is changing. It is changing all the time, due to external phenomena and due to internal properties.
The consequence is that something must be done: predict these changes and anticipate them. This leads to the tasks of model adaption and of prediction of the future. In DM II, we discuss these two tasks. |
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Comments | |
Description | Veranstaltungsbegin ist 9:30 Uhr. |
Literature | Scientific papers (to be announced at the course) |
Remarks | |
Prerequisites | Data Mining (recommended) |
Certificates | |
Target Group | WPF Master DKE WPF Master Inf WPF Master WIF WPF Master CV WPF Master IngInf WPF Master Statistik |
Description | Data Mining II - Advanced Topics in Data Mining |