Machine Learning UZ

Section outline

• Select section General

  General

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  • Select activity Learn how to mine your own data Today’s world gen...

    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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  • Select activity Teacher of this course assist. prof. Branko Kavše...

    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/

• Select section Additional Readings

  Additional Readings

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

    

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

    

    Data Mining and Analysis - free book URL
• Select section Lecture slides

  Lecture slides

  • Select activity Lecture 1 - What is it all about

    

    Lecture 1 - What is it all about File
  • Select activity Lecture 2 - Input: concepts, instances, attributes

    

    Lecture 2 - Input: concepts, instances, attributes File
  • Select activity Lecture 3 - Visualization

    

    Lecture 3 - Visualization File
  • Select activity Lecture 4 - Data preparation

    

    Lecture 4 - Data preparation File
  • Select activity Lecture 5 - Output: knowledge representation

    

    Lecture 5 - Output: knowledge representation File
  • Select activity Lecture 6 - Classification: simple methods

    

    Lecture 6 - Classification: simple methods File
  • Select activity Lecture 7 - Decision trees: introduction

    

    Lecture 7 - Decision trees: introduction File
  • Select activity Lecture 8 - Evaluation

    

    Lecture 8 - Evaluation File
• Select section The WEKA workbench

  The WEKA workbench

  • Select activity You can get WEKA for free from https://www.cs.waik...

    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.

• Select section Data repositories

  Data repositories

  • Select activity The UCI Machine Learning Repository: https://arc...

    • 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/.
• Select section Acknowledgement

  Acknowledgement

  • Select activity The teachers of this course gratefully acknowledge...

    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.