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 |
|---|---|---|---|---|---|---|---|
| Vorlesung(V) - Lecture - Dates/Times/Location: | |||||||
| Mon. | 13:00 bis 15:00 | weekly |
11.10.2021 to 24.01.2022 | G29-307 (Verwaltung durch FIN) | Spiliopoulou | 60 | |
| Übung (Ü) - Exercise - Dates/Times/Location: Group 1 | |||||||
| Thu. | 15:00 bis 17:00 | weekly | 14.10.2021 to 27.01.2022 | G29-336 (30 Pl.) |
Spiliopoulou
, Unnikrishnan | 20 | |
| Übung (Ü) - Exercise - Dates/Times/Location: Group 2 | |||||||
| Tue. | 13:00 bis 15:00 | weekly | 12.10.2021 to 25.01.2022 | G22A-128 (24 Pl.) | Jamaludeen | 1 | |
| Tue. | 13:00 bis 15:00 | Singular event | at 19.10.2021 | G23-K12 (24 Pl.) | 1 | ||
| Übung (Ü) - Exercise - Dates/Times/Location: Group 3 | |||||||
| Tue. | 13:00 bis 15:00 | weekly | 12.10.2021 to 25.01.2022 | Not a Public Person | |||
Overview (from LSF)
| Learning Content | In this course, we discuss advanced Data Mining methods for Data Science: * Dealing with VELOCITY: methods for supervised, semi-supervised and unsupervised learning on data streams * Dealing with VOLATILITY: learning and adaption on dynamic data * Dealing with VOLUME: methods for learning on high-dimensional data * VERACITY: incorporating expert knowledge into the learning process From the applications' perspective, we focus on web applications and on applications from the domain of medical research. |
|---|---|
| Comments | |
| Description | |
| 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 |
Lecture
- The lecture and exercises materials are distributed over Moodle. Click here to open the course page on Moodle.
Exercise
- The lecture and exercises materials are distributed over Moodle. Click here to open the course page on Moodle.