MPS logo Data Mining and Knowledge Discovery

professor: Nada Lavrač

assistant: Petra Kralj Novak



Programme: Information and Communication Technologies (ICT3)

Selected techniques for tabular and relational data mining / Izbrane tehnike rudarjenja tabelaričh in večrelacijskih podatkov (3 ct)

Data Mining and Knowledge Discovery / Podatkovno rudarjenje in odkrivanje zakonitosti (8/24 ct)

Programme: Statistics MSc program

Data mining / Podatkovno rudarjenje (5 ct)

Scheduled Lectures in 2011/12

Wednesday, 26. 10. 2011 15:15 - 17:00
Seminar I (ICT3): Knowledge Technologies - Data Mining
(prof. dr. Nada Lavrač)
IPS lecture hall
17:15 - 19:00
Selected techniques for tabular and relational data mining (ICT3)
(prof. dr. Nada Lavrač)
IPS lecture hall
Tuesday, 8. 11. 2011 17:15 - 19:00 Practice
(dr. Petra Kralj Novak)
IPS lecture hall
Tuesday, 15. 11. 2011
17:15 - 19:00 Data Mining and Knowledge Discovery (ICT2)
(N. Lavrač, B. Cestnik, D. Mladenić, P. Kralj Novak)
IPS lecture hall
Tuesday, 22. 11. 2011 17:15 - 19:00 Practice
(dr. Petra Kralj Novak)
IPS lecture hall
Tuesday, 29. 11. 2011 17:15 - 19:00 Practice
(dr. Petra Kralj Novak)
IPS lecture hall
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Statistika - Sodobni statistični pristopi - rudarjenje podatkov
(prof. dr. Nada Lavrač)
?
Tuesday, 20. 12. 2011 17:00 - 18:00 Data Mining written exam
(dr. Petra Kralj Novak)
IPS lecture hall
18:00 - 19:00 Data Mining seminar topic presentations
(dr. Petra Kralj Novak)
IPS lecture hall
Tuesday, 24. 1. 2012 17:15 - 19:00 Data mining seminar presentations
(prof. dr. Nada Lavrač, dr. Petra Kralj Novak)
IPS lecture hall

Course materials

Useful links

Course requirements:

Students Jožef Stefan IPS MSc (ICT2) course Data Mining and Knowledge Discovery (8/24 ct): written exam and seminar
Jožef Stefan IPS PhD (ICT3) course Selected techniques for tabular and relational data mining (3 ct): written exam
Statistics MSc program course Data mining (5 ct): written exam and seminar

  1. Written exam
  2. Data mining seminar
  3. Examples of data mining seminars:

    Ideas for seminars

    1. Analyze some data where you are the domain expert, use at least two algorithms
    2. Find some interesting data to analyze, possible sources:

    Templates


    Link to last year's web page - Data Mining and Knowledge Discovery 2010/2011
    Last update: 20120119