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Data Mining II - Advanced Topics in Data Mining

(in German: Data Mining II - Advanced Topics in Data Mining )

Module-ID: FIN-INF-120455
Link: LSF
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: Fachliche Spezialisierung
- M.Sc. VC: Computer Science

Abbreviation

DM2

Credit Points

6

Semester

Winter

Term

ab 1.

Duration

1 Semester

Language

english

Level

Master

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 1: Data Streams

  • Basics
  • Stream clustering: methods and evaluation approaches
  • Stream classification: learning methods and concept drift detectors; evaluation approaches
  • Semi-supervised stream learning: emthods and evaluation approaches
Block 2: Time series
  • Basics
  • Prediction methods
  • Evaluation of predictors

Workload:
56 h Präsenz + 124 h selbstständige Arbeit COURTESY TRANSLATION: 56 h in class + 124 h self-study

Pre-examination requirements: Type of examination: Teaching method / lecture hours per week (SWS):

  • Klausur 120 Minuten
  • Prüfungszulassungsvoraussetzung: Erreichen einer minimalen Anzahl von Punkten durch Votierung auf Übungsaufgaben
COURTESY TRANSLATION:
  • Written exam of the form 'Klausur' with a duration of 120 min
  • Prerequisite for the written exam: a minimum number of points must be achieved during the exercise classes; 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:

Block 1:

  • Gama, J. (2010) Knowledge discovery from data streams. CRC Press.
  • Silva, J. A., Faria, E. R., Barros, R. C., Hruschka, E. R., Carvalho, A. C. D., & Gama, J. (2013). Data stream clustering: A survey. ACM Computing Surveys (CSUR), 46(1), 1-31.
  • Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014). A survey on concept drift adaptation. ACM computing surveys (CSUR), 46(4), 1-37.
  • Lemaire, V., Salperwyck, C., & Bondu, A. (2014, July). A survey on supervised classification on data streams. In European Business Intelligence Summer School (pp. 88-125). Springer, Cham.
  • Žliobaitė, I., Bifet, A., Read, J., Pfahringer, B., & Holmes, G. (2015). Evaluation methods and decision theory for classification of streaming data with temporal dependence. Machine Learning, 98(3), 455-482.
  • Murilo Gomes, H., Grzenda, M., Mello, R., Read, J., Nguyen, M. H. L., and Bifet, A. (2022). A survey on semi-supervised learning for delayed partially labelled data streams. ACM Computing Surveys, 55(4). - for Block 1D
Block 2:
  • Parmezan, A. R. S., Souza, V. M., & Batista, G. E. (2019). Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model. Information sciences, 484, 302-337.
  • Cismondi, F. C., Fialho, A., Vieira, S., Reti, S., Sousa, J., Finkelstein, S. (2013). Missing data in medical databases: Impute, delete or classify? Artificial Intelligence in Medicine.
    http://dx.doi.org/10.1016/j.artmed.2013.01.003.
  • Further citations at the individual units: see the slidesets of the units.

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