# Data Mining II - Advanced Topics in Data Mining ##### Winter ##### (in German: Data Mining II - Advanced Topics in Data Mining )
##### **Module-ID:** **FIN-INF-120455**
**Abbreviation** DM2 **Credit Points** 9 **Semester** Winter **Term** starting at 1. **Duration** 1 Semester **Language** english **Level** Master
**Link:**[ LSF](https://lsf.ovgu.de/qislsf/rds?state=wsearchv&search=1&subdir=veranstaltung&veranstaltung.semester=20262&veranstaltung.dtxt=Data+Mining+II+-+Advanced+Topics+in+Data+Mining&P_start=0&P_anzahl=30&P.sort=veranstaltung.dtxt&_form=display "Data Mining II - Advanced Topics in Data Mining")
**Limited accessability:**false
**Responsibility:**Myra Spiliopoulou
**Lecturer:**Myra Spiliopoulou
**Classes:**Vorlesung DM 2 Übung DM 2 **COURTESY TRANSLATION:**- Lecture class DM2 - Exercise class DM2
**Applicability in curriculum:**- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Learning Methods and Models for Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Fachliche Spezialisierung - M.Sc. VC: Computer Science
**Intended learning outcomes:** When successfully completing this module, the students: - comprehend why temporal data need different learning algorithms and evaluation procedures than used on static data - comprehend the behaviour of supervised, unsupervised and semi-supervised learning algorithms on temporal data - can design and apply simple learning algorithms and workflows on temporal data and interpret the induced models - can evaluate models - once and in continuous evaluation, since both are needed in temporal learning and have thus acquired skills they need in order to design and evaluate temporal learning algorithms themselves **Content:** Block 1A: Data Streams - Basics - Stream classification: learning methods and concept drift detectors; evaluation approaches - Semi-supervised stream learning: methods and evaluation approaches Block 1B: Time series - Basics - Time series classification - Time series prediction - Evaluation of models Block 2: discussion of papers (inverted classroom) and hands-on of algorithms (in the exercises) **Workload:** - 28 hours - in presence : lecture class (2 hours per week) - 28 hourse in presence: exercise class (2 hours per week) - 214 Stunden: Individual working time on - Preparation for lectures and exercise classes - Reading and understanding the articles (scientific papers) provided for the discussion in the lecture class
**Pre-examination requirements:****Type of examination:****Teaching method / lecture hours per week (SWS):**
Votierung in der Vorlesung und in der Übung (A minimum number of points must be achieved in Block 1 and in Block 2) Schriftliche Prüfung - Klausur **COURTESY TRANSLATION:** - Written exam of the form 'Klausur' - Prerequisite for the written exam: a minimum number of points must be achieved; this procedure is called 'Votierung' - Vorlesung (2 SWS) - Übung (2 SWS) **COURTESY TRANSLATION:**- Lecture (2 hours per week of the semester) - Exercise (2 hours per week of the semester)
**Prerequisites according to examination regulations:****Recommended prerequisites:**
keine - Familiarity with learning algorithms for static tabular data - Familiarity with methods for the evaluation of models induced on static data
**Media:****Literature:**
**Comments:** Block 1: Literature on streams and time series; is provided in the elearning page of the module Block 2: New papers are discussed, every time the course is offered