Data Mining II - Advanced Topics in Data Mining
Information about the inspection of the exam paper is available here.
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 | G22A-210 (24 Pl.) | Spiliopoulou | 20 | ||
Übung (Ü) - Exercise - Dates/Times/Location: | |||||||
Thu. | 09:00 bis 11:00 | weekly | G22A-210 (24 Pl.) | Tutor | 20 |
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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Literature | Scientific papers (to be announced at the course) |
Prerequisites | Data Mining (recommended) |
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
Block 1a - Mining Volatile Data (UPDATE 15.05.2017)
Block 1b - Mining Volatile Data
Block 1c - Mining Volatile Data
Block 1c - Part 2 (UPDATE: 19.06.2017)
Exercise
Exercises - General Information