SLO | EN

Objectives and competences

Student will: • Understand the role of KDD. • Define the process of problem solving. • Independently organize the KDD process with emphasis on specific methods of data mining. • Argument and present the results to various publics.

Content (Syllabus outline)

1. Introduction to data mining. 2. Use cases of data mining. 3. Process of knowledge from data discovery, data mining. 4. Input: Concepts, instances, attributes. 5. Output: Knowledge Representation. 6. CRISP-DM standard. 7. Data preparation. 8. Data visualization. 9. Machine learning (Supervized learning, Unsupervized learning, Semi-supervized learning, Deep Learning) 10. Algorithms: Decision tree, Classification, Regression, Instance based algorithms, Naive Bayes, neural networks, clustering, genetic algorithms. 11. Model evaluation and Implementation. 12. Overview of data mining tools (Orange, Weka, R).

Learning and teaching methods

• Lectures • Team work • Case studies • Exercises • Project work

Intended learning outcomes - knowledge and understanding

Knowledge and understanding: After completing the course the students will be able to: • Formulate the data mining problem. • Select an appropriate data mining method. • Prepare the data from available sources. • Develop the model. • Critically evaluate the model. • Interpret the results. • Present the results.

Intended learning outcomes - transferable/key skills and other attributes

- To prepare the data from the available sources. - To choose the appropriate data mining method, do the data mining process, and critically evaluate and interpret the solution.

Readings

Temeljna literatura: 1. Provost, F. & Fawcett, T. (2013): Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking 1st Edition, O'Reilly 2. Zaki, M. J., Wagner, M. Jr.: Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press, New York, 2014. (izbrana poglavja) 3. Leskovec, J., Rajaraman, A., Ullman, J. D.: Mining of Massive Datasets. Cambridge University Press, New York, 2014. (izbrana poglavja) 4. Kononenko, I., Robnik Šikonja, M.: Inteligentni sistemi. Založba FE in FRI, Ljubljana, 2010. 5. Bramer, M.A. Principles of data mining. London : Springer, 2007. 6. Han, J., Kamber, M. Data mining, concepts and techniques, 3nd edition. Morgan Kaufmann Publishers, Elsevier, San Francisco, 2012. 7. Tan, P.N., Steinbach, M., Kumar, V. Introduction to data mining. Boston, Pearson Addison Wesley, 2006. 8. Witten, I.H., Frank, E.: Data mining Practical Machine Learning Tools and Techniques, Elsevier, 2005.

Prerequisits

/ Conditions for exam admission: Completed assignments at lectures and tutorials. Completed project work.

  • red. prof. dr. MIRJANA KLJAJIĆ BORŠTNAR, univ. dipl. org.

  • Pisni izpit: 50
  • Projektna naloga: 30
  • Opravljene domače naloge: 20

  • : 36
  • : 24
  • : 180

  • slovensko
  • slovensko

  • ORGANIZACIJA IN MANAGEMENT INFORMACIJSKIH SISTEMOV - 2.