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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
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

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