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dc.contributor.authorGarcía Algarra, Francisco Javieres-ES
dc.date.accessioned2020-09-14T08:55:47Z
dc.date.available2020-09-14T08:55:47Z
dc.identifier.urihttp://hdl.handle.net/11531/50540
dc.description.abstractes-ES
dc.description.abstractData Science in general, and Machine Learning in particular, is a powerful tool for decision-makers across non-STEM (Sience, Techonology, Engineering and Maths) fields like Human Resources Management, Law or Marketing. Introductory Machine Learning, for non-majors that lack a strong background in Statistics and Computer Science, is a challenge for both teacher and students. The use of similes and games is a soft way to deal with definitions of concepts and procedures that are essential for further advanced courses on these subjects.en-GB
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoes-ESes_ES
dc.rightsCreative Commons Reconocimiento-NoComercial-SinObraDerivada Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/es_ES
dc.titleIntroductory Machine Learning for Non STEM Studentses_ES
dc.typeinfo:eu-repo/semantics/workingPaperes_ES
dc.description.versioninfo:eu-repo/semantics/draftes_ES
dc.rights.holderes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.keywordses-ES
dc.keywordsen-GB


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