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master's thesis
Otkrivanje nepravilnosti u sportskom klađenju strojnim učenjem
2022. urn:nbn:hr:168:350951
Teofilović, Ivan
University of Zagreb
Faculty of Electrical Engineering and Computing

Cite this document

Teofilović, I. (2022). Otkrivanje nepravilnosti u sportskom klađenju strojnim učenjem (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:350951

Teofilović, Ivan. "Otkrivanje nepravilnosti u sportskom klađenju strojnim učenjem." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:350951

Teofilović, Ivan. "Otkrivanje nepravilnosti u sportskom klađenju strojnim učenjem." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:350951

Teofilović, I. (2022). 'Otkrivanje nepravilnosti u sportskom klađenju strojnim učenjem', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 18 April 2024, https://urn.nsk.hr/urn:nbn:hr:168:350951

Teofilović I. Otkrivanje nepravilnosti u sportskom klađenju strojnim učenjem [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 April 18] Available at: https://urn.nsk.hr/urn:nbn:hr:168:350951

I. Teofilović, "Otkrivanje nepravilnosti u sportskom klađenju strojnim učenjem", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:350951

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Metadata
TitleOtkrivanje nepravilnosti u sportskom klađenju strojnim učenjem
Title (english)Anomaly detection in sports betting industry using machine learning
AuthorIvan Teofilović
MentorDina Šimunić (mentor)
Committee memberDina Šimunić (predsjednik povjerenstva)
Committee memberKrešimir Malarić (član povjerenstva)
Committee memberRadovan Zentner (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-07-06, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Otkrivanje nepravilnosti je postupak otkrivanja podataka koji znatno odstupaju od 'normalnih' podataka unutar skupa. Povećanje broja dostupnih podataka, poboljšanje računalnih resursa te istraživanje novih algoritama čine strojno učenje standardom u otkrivanju nepravilnosti (anomalija). Cilj diplomskog rada je uspješno primijeniti model strojnog učenja na otkrivanje nepravilnosti u sportskom klađenju, a u svrhu poboljšanja kvalitete koeficijenata te profita tvrtke.
Abstract (english)
Anomaly detection is a process of identifying data points that deviate significantly from a dataset's "normal" behavior. With the increase in available data, the performance of computing resources and research in algorithms machine learning is becoming standard to automate anomaly detection. The goal of this master thesis is to successfully apply a machine learning model to detect anomalies in the sports betting industry in order to increase the odds quality and hence the profitability of the Company.
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:350951
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsClosed access
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Created on2023-01-13 16:22:18