03329ntm a22005297i 4500 000714264 CZ-PrVSE 20240615111821.0 m d cr n|||||||||| 240615s2024 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Nechiporuk, Andrei ISIS:162388 dis Strojové učení pomocí knihoven Qiskit a squlearn eng Machine learning using libraries Qiskit and squlearn / Andrei Nechiporuk 2024 ?? stran : digital, PDF soubor Vedoucí práce: Tomáš Kliegr Bakalářská práce (Bc.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2024 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2024 The bachelor thesis deals with the use and comparison of quantum and classical machine learning techniques, specifically focusing on classification tasks for the diagnosis of Parkinson’s disease. Using current frameworks such as qiskit and squlearn for quantum machine learning and scikit-learn for classical approaches. The study assesses how these technologies handle and analyze datasets. The aim is to perform a detailed review and comparison of quantum algorithms versus classical methods, focusing on their performance in classification tasks, specifically with respect to evaluation metrics such as precision, recall and F1-score. The work is structured in different stages, starting with a theoretical introduction to quantum machine learning, followed by data preprocessing and feature selection using mutual information, and the implementation and evaluation of both the support vector machine classifier and its quantum equivalent, the QSVC algorithm. These models are tested on two datasets and the results are compared with previous research. The paper concludes with a discussion of the comparative results, a control test using the Oxford Parkinson’s disease detection dataset, and an examination of the important attributes and their effect on the predictive performance of the models. Způsob přístupu: Internet aplikovaná informatika [obor bakal. práce] bakalářské práce fd132403 czenas bachelor's theses eczenas quantum machine learning classical machine learning Parkinson’s disease qiskit squlearn scikit-learn Kliegr, Tomáš ISIS:8484 ths Berka, Petr, 1959- jn20001103427 opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/84704/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/84704 Hlavní práce https://insis.vse.cz/zp/84704/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/84704/posudek/oponent/82356 Oponentura https://insis.vse.cz/zp/84704/podrobnosti dc:identifier NEPOSILAT VSKP vse84704 240611 84704