[ 921DASICDAK17 ] KV Computational Data Analytics

Workload Education level Study areas Responsible person Hours per week Coordinating university
3 ECTS M1 - Master's programme 1. year Computer Science Johannes F├╝rnkranz 2 hpw Johannes Kepler University Linz
Detailed information
Original study plan Master's programme Computer Science 2022W
Objectives 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.
  • 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
Criteria for evaluation Written Exam at the end of the semester, Project assignment
Methods Slide Presentations with Practical Exercises
Language English
Study material 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.

Changing subject? No
On-site course
Maximum number of participants -
Assignment procedure Direct assignment