Faculty of Technical Sciences

Subject: Data mining methods (17.IZOI62)

General information:
 
Category Professional-applicative
Scientific or art field
  • Computer Engineering and Computer Communication
  • Inženjerstvo informacionih sistema
  • Information-Communication Systems
ECTS 6

To impart basic knowledge of machine learning algorithms and methods used in the domain of data mining.

Upon successful completion of the course the students will have acquired knowledge and skills that will enable them to efficiently apply basic techniques of artificial intelligence and machine learning to mine data. They will be introduced to various aspects of computers as data mining tools, structural pattern discovery, presentation and use of knowledge discovered.

The course will cover the following areas: an overview of the basic concepts of data mining, data sources and preprocessing, decision trees, neural networks, support vector machines, clustering, time series analysis. Theoretical instruction will be accompanied by practical training in the use of open source data mining solutions.

Lectures and labs, tests and an exam assignment. The labs will focus on enabling the students to use the Java programming language to implement computer programs. The students’ knowledge of the theory will be evaluated using tests. The individual assignment will consist of the practical implementation of programs of suitable complexity.

Authors Title Year Publisher Language
Tan, P.N., Steinbach, M., Kumar, V. Introduction to Data Mining 2006 Pearson, Boston English
Ian H. Witten, Eibe Frank, Mark A. Hall Data Mining: Practical Machine Learning Tools and Techniques 2011 Morgan Kaufmann English
Witten, I., Frank, E., Hall, M.A., Pal, J.C. Data Mining Practical Machine Learning Tools and Techniques 2017 Morgan Kaufmann, Amsterdam English
Goodfellow, I., Bengio, Y., Courville, A. Deep Learning 2017 MIT Press, Cambridge English
Course activity Pre-examination Obligations Number of points
Project Yes Yes 40.00
Test Yes Yes 10.00
Test Yes Yes 10.00
Oral part of the exam No Yes 30.00
Test Yes Yes 10.00
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Prof. Sladojević Srđan

Full Professor

Lectures
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Prof. Ćulibrk Dubravko

Full Professor

Lectures
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Asst. Prof. Arsenović Marko

Assistant Professor

Lectures
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Assistant - Master Kijanović Sara

Assistant - Master

Computational classes
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Teaching Associate Melović Sandra

Teaching Associate

Computational classes

Assistant - Master Spasojević Ivana

Assistant - Master

Computational classes

Faculty of Technical Sciences

© 2024. Faculty of Technical Sciences.

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(+381) 21 6350 413

Fax : (+381) 21 458 133
Emejl: ftndean@uns.ac.rs

© 2024. Faculty of Technical Sciences.