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Title:Uporaba strojnega učenja v programskem inženirstvu
Authors:ID Tomić, Ivan (Author)
ID Kokol, Peter (Mentor) More about this mentor... New window
Files:.pdf MAG_Tomic_Ivan_2024.pdf (2,49 MB)
MD5: 8C7EE1DFD3DD2F6F5C54DCCBCA6BDC23
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V magistrskem delu je predstavljena uporaba strojnega učenja v programskem inženirstvu. Strojno učenje nam omogoča pridobivanje dragocenih informacij in ustvarjanje napovednih modelov, ki prispevajo k razvoju številnih rešitev na različnih področjih. Eno od teh področji je tudi programsko inženirstvo, kjer nam lahko strojno učenje pomaga izboljšati učinkovitost, pohitriti razvoj in zmanjšati število napak. Z vse večjim številom aplikacij, ki vključujejo strojno učenje pa narašča tudi potreba po razvoju bolj učinkovitih postopkov pri izdelavi programske opreme za te namene. Zato smo v tem magistrskem delu raziskali kako oblikovati postopke za razvoj programske opreme, ki temelji na strojnem učenju, ter predstavili sodobna orodja strojnega učenja za optimalen razvoj AI aplikacij.
Keywords:Strojno učenje, Programsko inženirstvo, Optimizacija razvojnih procesov, Orodja umetne inteligence
Place of publishing:Maribor
Publisher:[I. Tomić]
Year of publishing:2024
PID:20.500.12556/DKUM-90237 New window
UDC:004.85:004.41(043.2)
COBISS.SI-ID:224206595 New window
Publication date in DKUM:22.10.2024
Views:222
Downloads:72
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:27.08.2024

Secondary language

Language:English
Title:Software engineering for machine learning
Abstract:The master's thesis presents challenges in software engineering for machine learning. Machine learning enables us to obtain valuable information and create predictive models that contribute to the development of various solutions in different fields. One of these fields is also software engineering, where machine learning can help us improve efficiency, speed up development and reduce the number of errors. With an increasing number of applications incorporating machine learning, the need to develop more efficient software development processes is also increasing. Therefore, in this master's thesis, we explored how to design procedures for the development of software based on machine learning and presented modern machine learning tools for the optimal development of AI applications.
Keywords:Machine learning, Software engineering, Optimization of development processes, Artificial Intelligence Tools


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