An Undergraduate Curriculum for Deep Learning

dc.authorid Turkmen, Guzin/0000-0003-0884-4876
dc.authorid Ekin, Cansu Cigdem/0000-0003-4838-9708
dc.authorid Bostan, Atila/0000-0002-8540-7605
dc.authorwosid Şengül, Gökhan/AAA-2788-2022
dc.authorwosid Ekin, cansu/AFE-7836-2022
dc.authorwosid KARAKAYA, Murat/A-4952-2013
dc.authorwosid Bostan, Atila/AAY-2187-2020
dc.authorwosid Turkmen, Guzin/G-9033-2019
dc.authorwosid Bostan, Atila/O-6678-2018
dc.contributor.author Tirkes, Guzin
dc.contributor.author Ekin, Cansu Cigdem
dc.contributor.author Sengul, Gokhan
dc.contributor.author Bostan, Atila
dc.contributor.author Karakaya, Murat
dc.contributor.other Computer Engineering
dc.date.accessioned 2024-10-06T10:58:28Z
dc.date.available 2024-10-06T10:58:28Z
dc.date.issued 2018
dc.department Atılım University en_US
dc.department-temp [Tirkes, Guzin; Ekin, Cansu Cigdem; Sengul, Gokhan; Bostan, Atila; Karakaya, Murat] Atilim Univ, Dept Comp Engn, Ankara, Turkey en_US
dc.description Turkmen, Guzin/0000-0003-0884-4876; Ekin, Cansu Cigdem/0000-0003-4838-9708; Bostan, Atila/0000-0002-8540-7605 en_US
dc.description.abstract Deep Learning (DL) is an interesting and rapidly developing field of research which has been currently utilized as a part of industry and in many disciplines to address a wide range of problems, from image classification, computer vision, video games, bioinformatics, and handwriting recognition to machine translation. The starting point of this study is the recognition of a big gap between the sector need of specialists in DL technology and the lack of sufficient education provided by the universities. Higher education institutions are the best environment to provide this expertise to the students. However, currently most universities do not provide specifically designed DL courses to their students. Thus, the main objective of this study is to design a novel curriculum including two courses to facilitate teaching and learning of DL topic. The proposed curriculum will enable students to solve real-world problems by applying DL approaches and gain necessary background to adapt their knowledge to more advanced, industry-specific fields. en_US
dc.description.woscitationindex Conference Proceedings Citation Index - Science
dc.identifier.citationcount 1
dc.identifier.endpage 609 en_US
dc.identifier.isbn 9781538678930
dc.identifier.scopusquality N/A
dc.identifier.startpage 604 en_US
dc.identifier.uri https://hdl.handle.net/20.500.14411/8908
dc.identifier.wos WOS:000459847400116
dc.identifier.wosquality N/A
dc.institutionauthor Türkmen, Güzin
dc.institutionauthor Ekin, Cansu Çiğdem
dc.institutionauthor Şengül, Gökhan
dc.institutionauthor Bostan, Atila
dc.institutionauthor Karakaya, Kasım Murat
dc.language.iso en en_US
dc.publisher Ieee en_US
dc.relation.ispartof 3rd International Conference on Computer Science and Engineering (UBMK) -- SEP 20-23, 2018 -- Sarajevo, BOSNIA & HERCEG en_US
dc.relation.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject Deep Learning en_US
dc.subject curriculum design en_US
dc.subject Machine Learning en_US
dc.subject Deep Neural Networks en_US
dc.subject Engineering Education en_US
dc.title An Undergraduate Curriculum for Deep Learning en_US
dc.type Conference Object en_US
dc.wos.citedbyCount 1
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