Rik Vegter

Education

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  Winkler Prins

  2010 - 2016

  VWO Natuur/techniek
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  Rijksuniversiteit Groningen

  2020 - 2022

  Master Artificial Intelligence 8.0

  My master's thesis called "Optimizing the financial gain for credit card fraud detection systems using machine learning techniques" handles the subject of classification of credit card fraud data. The goal was to correctly classify possible fraudulent credit card transactions using state-of-the-art machine learning techniques. The evaluation of the systems were done with a newly proposed financial metric called 'Economic Efficiency'.One of the main conclusions was that optimizing a CFD… Show more My master's thesis called "Optimizing the financial gain for credit card fraud detection systems using machine learning techniques" handles the subject of classification of credit card fraud data. The goal was to correctly classify possible fraudulent credit card transactions using state-of-the-art machine learning techniques. The evaluation of the systems were done with a newly proposed financial metric called 'Economic Efficiency'.One of the main conclusions was that optimizing a CFD system based on the number of correctly classified transactions does not necessarily mean the financial gain is optimized.This thesis was graded with an 8.0Link: https://fse.studenttheses.ub.rug.nl/28941/ Show less

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  Nyenrode Business Universiteit

  2024 - 2024

  Artificial Intelligence & Business Information Technology

  Waar zitten de toepassingen van Artificial Intelligence bij bedrijven? Waar moeten we voor waken bij de implementatie van deze systemen? Waar moet je rekening mee houden als het gaat om AI zodat een bedrijf er echt iets aan heeft? Deze vragen en meer kwamen aan bod bij de collegereeks

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  Rijksuniversiteit Groningen

  2016 - 2020

  Bachelor's degree Artificial Intelligence 7.5

  My Bachelor thesis called "Emotion detection using machine learning on EEG data" was about the classification of brain activity to classify a human's emotional state. This was done by extracting features from the time domain of EEG signals. These features were preprocessed for optimal classification performance and afterwards were classified using several machine learning techniques. This thesis was graded with a 7.0