An Undergraduate Curriculum for Deep Learning

dc.authoridTurkmen, Guzin/0000-0003-0884-4876
dc.authoridEkin, Cansu Cigdem/0000-0003-4838-9708
dc.authoridBostan, Atila/0000-0002-8540-7605
dc.authorwosidŞengül, Gökhan/AAA-2788-2022
dc.authorwosidEkin, cansu/AFE-7836-2022
dc.authorwosidKARAKAYA, Murat/A-4952-2013
dc.authorwosidBostan, Atila/AAY-2187-2020
dc.authorwosidTurkmen, Guzin/G-9033-2019
dc.authorwosidBostan, Atila/O-6678-2018
dc.contributor.authorTirkes, Guzin
dc.contributor.authorEkin, Cansu Cigdem
dc.contributor.authorSengul, Gokhan
dc.contributor.authorBostan, Atila
dc.contributor.authorKarakaya, Murat
dc.contributor.otherComputer Engineering
dc.date.accessioned2024-10-06T10:58:28Z
dc.date.available2024-10-06T10:58:28Z
dc.date.issued2018
dc.departmentAtılım Universityen_US
dc.department-temp[Tirkes, Guzin; Ekin, Cansu Cigdem; Sengul, Gokhan; Bostan, Atila; Karakaya, Murat] Atilim Univ, Dept Comp Engn, Ankara, Turkeyen_US
dc.descriptionTurkmen, Guzin/0000-0003-0884-4876; Ekin, Cansu Cigdem/0000-0003-4838-9708; Bostan, Atila/0000-0002-8540-7605en_US
dc.description.abstractDeep 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.woscitationindexConference Proceedings Citation Index - Science
dc.identifier.citation1
dc.identifier.doi[WOS-DOI-BELIRLENECEK-101]
dc.identifier.endpage609en_US
dc.identifier.isbn9781538678930
dc.identifier.scopusqualityN/A
dc.identifier.startpage604en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14411/8908
dc.identifier.wosWOS:000459847400116
dc.identifier.wosqualityN/A
dc.institutionauthorTürkmen, Güzin
dc.institutionauthorEkin, Cansu Çiğdem
dc.institutionauthorŞengül, Gökhan
dc.institutionauthorBostan, Atila
dc.institutionauthorKarakaya, Kasım Murat
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof3rd International Conference on Computer Science and Engineering (UBMK) -- SEP 20-23, 2018 -- Sarajevo, BOSNIA & HERCEGen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep Learningen_US
dc.subjectcurriculum designen_US
dc.subjectMachine Learningen_US
dc.subjectDeep Neural Networksen_US
dc.subjectEngineering Educationen_US
dc.titleAn Undergraduate Curriculum for Deep Learningen_US
dc.typeConference Objecten_US
dspace.entity.typePublication
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