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: | |||||||
Tue. | bis | weekly | Spiliopoulou | ||||
Übung (Ü) - Exercise - Dates/Times/Location: | |||||||
Thu. | bis | weekly | Unnikrishnan |
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. |
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Comments | New exam form: Elektronische schriftliche Prüfung (unbeaufsichtigt), i.e. digital unsupervised open-book exam |
Description | New exam form: Elektronische schriftliche Prüfung (unbeaufsichtigt), i.e. digital unsupervised open-book exam |
Literature | Scientific papers (to be announced at the course) |
Remarks | New exam form: Elektronische schriftliche Prüfung (unbeaufsichtigt), i.e. digital unsupervised open-book exam |
Prerequisites | Data Mining (recommended) |
Certificates | New exam form: Elektronische schriftliche Prüfung (unbeaufsichtigt), i.e. digital unsupervised open-book exam |
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 |
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.