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Introductory Machine Learning for Non STEM Students
dc.contributor.author | García Algarra, Francisco Javier | es-ES |
dc.date.accessioned | 2020-09-14T08:55:47Z | |
dc.date.available | 2020-09-14T08:55:47Z | |
dc.identifier.uri | http://hdl.handle.net/11531/50540 | |
dc.description.abstract | es-ES | |
dc.description.abstract | Data 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.mimetype | application/pdf | es_ES |
dc.language.iso | es-ES | es_ES |
dc.rights | Creative Commons Reconocimiento-NoComercial-SinObraDerivada España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | es_ES |
dc.title | Introductory Machine Learning for Non STEM Students | es_ES |
dc.type | info:eu-repo/semantics/workingPaper | es_ES |
dc.description.version | info:eu-repo/semantics/draft | es_ES |
dc.rights.holder | es_ES | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.keywords | es-ES | |
dc.keywords | en-GB |
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