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050035 VU Machine Learning (2011S)

Continuous assessment of course work

Details

max. 25 participants
Language: German

Lecturers

Classes (iCal) - next class is marked with N

Friday 11.03. 16:30 - 18:00 (ehem. Hörsaal DAC Universitätsstraße 5 Hochparterre)
Friday 18.03. 16:30 - 18:00 (ehem. Hörsaal DAC Universitätsstraße 5 Hochparterre)
Friday 25.03. 16:30 - 18:00 (ehem. Hörsaal DAC Universitätsstraße 5 Hochparterre)
Friday 01.04. 16:30 - 18:00 (ehem. Hörsaal DAC Universitätsstraße 5 Hochparterre)
Friday 08.04. 16:30 - 18:00 (ehem. Hörsaal DAC Universitätsstraße 5 Hochparterre)
Friday 15.04. 16:30 - 18:00 (ehem. Hörsaal DAC Universitätsstraße 5 Hochparterre)
Friday 06.05. 16:30 - 18:00 (ehem. Hörsaal DAC Universitätsstraße 5 Hochparterre)
Friday 13.05. 16:30 - 18:00 (ehem. Hörsaal DAC Universitätsstraße 5 Hochparterre)
Friday 20.05. 16:30 - 18:00 (ehem. Hörsaal DAC Universitätsstraße 5 Hochparterre)
Friday 27.05. 16:30 - 18:00 (ehem. Hörsaal DAC Universitätsstraße 5 Hochparterre)
Friday 03.06. 16:30 - 18:00 (ehem. Hörsaal DAC Universitätsstraße 5 Hochparterre)
Friday 10.06. 16:30 - 18:00 (ehem. Hörsaal DAC Universitätsstraße 5 Hochparterre)
Friday 17.06. 16:30 - 18:00 (ehem. Hörsaal DAC Universitätsstraße 5 Hochparterre)
Friday 24.06. 16:30 - 18:00 (ehem. Hörsaal DAC Universitätsstraße 5 Hochparterre)

Information

Aims, contents and method of the course

Basic methods in machine learning: Supervised Learning (classification), Unsupervised Learning (Cluster analysis), Incomplete Data Problems (EM-Algorithm), Assoziation rules, Page Rank,

Assessment and permitted materials

Attandence of lectures, solving and presentation of practical exercises (50%), final test (50%)

Minimum requirements and assessment criteria

getting familiar with basic ideas in machine learning and application of the methods wit R and Weka.

Examination topics

Lectures with parctical exercises, mainly by using R and Weka.

Reading list

X. Wu, V. Kumar: The Top Ten Algorithms in Data Mining, Chapman&Hall/CRC Data Mining and Knowledge Discovery Series, 2009
Hastie-Tibshirani-Friedman: The Elements of Statistical Learning, Springer 2001
Cherkassky-Mulier: Learning from Data, IEEE Press, Wiley 2007

Association in the course directory

Last modified: Mo 07.09.2020 15:29