Machine Learning UZ

Topic outline

• General

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  • Learn how to mine your own data

    Today’s world generates more data than ever before! Being able to turn it into useful information is a key skill. This short course introduces you to basic theoretical, as well as, practical data mining using the WEKA workbench. We’ll dispel the mystery that surrounds the subject. We’ll briefly explain the theoretical principles of some popular algorithms. We’ll show you how to use them on selected sample datasets. You’ll get some experience actually mining data during this course, and afterwards you’ll be equipped to mine your own.

    WEKA originated at the University of Waikato in New Zealand. Its founder, prof. Ian H. Witten has authored a leading book on data mining. In this course, you'll be using WEKA. More detailed information on presented algorithms and methods can be found in the book (see the Additional Readings section of this course).

    Have fun!

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  • Teacher of this course

    assist. prof. Branko Kavšek
    e-mail: branko.kavsek@upr.si
    web: https://www.famnit.upr.si/sl/zaposleni-in-sodelavci/branax/

• Additional Readings
  • 

    URL

    Data Mining: Practical Machine Learning Tools and Techniques (4th ed.) - table of contents URL
  • 

    URL

    Data Mining and Analysis - free book URL

• Lecture slides
  • 

    File

    Lecture 1 - What is it all about File
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    File

    Lecture 2 - Input: concepts, instances, attributes File
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    File

    Lecture 3 - Visualization File
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    File

    Lecture 4 - Data preparation File
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    File

    Lecture 5 - Output: knowledge representation File
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    File

    Lecture 6 - Classification: simple methods File
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    File

    Lecture 7 - Decision trees: introduction File
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    File

    Lecture 8 - Evaluation File

• The WEKA workbench
  • You can get WEKA for free from https://www.cs.waikato.ac.nz/ml/weka/ (under the download section). You may find other related resources on this webpage.

• Data repositories
  • • The UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/index.php,
    • The Kaggle ML Repository: https://www.kaggle.com/,
    • The FigShare Repository: https://figshare.com/.

• Acknowledgement
  • The teachers of this course gratefully acknowledge the European Commission for funding the InnoRenew CoE project (Grant Agreement #739574) under the Horizon 2020 Widespread-Teaming program and the Republic of Slovenia (Investment funding of the Republic of Slovenia and the European Union of the European Regional Development Fund). They also acknowledge the Slovenian Research Agency ARRS for funding the project J2-2504.