The development, enhancement opportunities, and measurement of algorithmic thinking skills with the application of machine learning and data mining methods
Thesis supervisor: Tibor Gábor Tajti
Location of studies (in Hungarian): University of Debrecen Abbreviation of location of studies: ITDI
Description of the research topic:
The research aims to analyze the development, enhancement opportunities, and measurement of algorithmic thinking skills in computer science education. The goal is to develop a new testing system that integrates machine learning algorithms and data analysis methods for international comparisons. The research extends to the examination of joint methods and the application of fuzzification of binary classification data, contributing to the improvement of the effectiveness of computer science education.
Bibliography
[1] Dietterich, Thomas G. "Ensemble methods in machine learning." International workshop on multiple classifier systems. Springer, Berlin, Heidelberg, 2000.
[2] Sagi, Omer, and Lior Rokach. "Ensemble learning: A survey." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8.4 (2018): e1249.
[3] Zhou, Zhi-Hua. Ensemble methods: foundations and algorithms. Chapman and Hall/CRC, 2019.
[4] Opitz, David, and Richard Maclin. "Popular ensemble methods: An empirical study." Journal of artificial intelligence research 11 (1999) 169-198
Deadline for application: 2024-05-15
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).
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