Inhalt

[ 921DASICDAK17 ] KV (*)Computational Data Analytics

Versionsauswahl
(*) Leider ist diese Information in Deutsch nicht verfügbar.
Workload Ausbildungslevel Studienfachbereich VerantwortlicheR Semesterstunden Anbietende Uni
3 ECTS M1 - Master 1. Jahr Informatik Johannes Fürnkranz 2 SSt Johannes Kepler Universität Linz
Detailinformationen
Quellcurriculum Masterstudium Computer Science 2024W
Ziele (*)Students master foundational concepts and techniques of machine learning and data mining. They are able to competently use data mining software on practical problems, and have a thorough theoretical understanding, which enables them to implement such methods on their own. In particular, they are also familiar with the challenges of big data.
Lehrinhalte (*)
  • Data mining process models
  • Pre-processing techniques
  • Inductive rule learning
  • Efficient similarity-based techniques
  • Clustering for big data
  • Association rule mining
  • Foundations of Stream Mining
  • Evaluation
Beurteilungskriterien (*)Written Exam at the end of the semester
Lehrmethoden (*)Slide Presentations with Practical Exercises
Abhaltungssprache Englisch
Literatur (*)I. H. Witten, E. Frank, M. A. Hall, C. J. Pal: Data Mining. Morgan Kaufmann.

J. Leskovec, A. Rajaraman, J. D. Ullman: Mining of Massive Datasets. Cambridge University Press.

Lehrinhalte wechselnd? Nein
Sonstige Informationen (*)The lecture is accompanied with a voluntary practical course (351.044), in which interested students can collect experience with practical data mining tools such as Weka.
Präsenzlehrveranstaltung
Teilungsziffer -
Zuteilungsverfahren Direktzuteilung