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dc.contributor.advisorHarold Abelson.en_US
dc.contributor.authorZhu, Kevin(Kevin F.)en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2019-11-22T00:00:21Z
dc.date.available2019-11-22T00:00:21Z
dc.date.copyright2019en_US
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/122989
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 81-82).en_US
dc.description.abstractMachine learning has increasingly become a major topic in computer science for students to learn. However, it can be quite technical and thus difficult for students to grasp, especially those in high school and under. To make machine learning and its applications more accessible to younger students, we developed a series of machine learning extensions for MIT App Inventor. MIT App Inventor is a web application for users with minimal programming experience to easily and quickly build mobile applications, and these extensions allow users to build applications that incorporate powerful machine learning functionality. These extensions were tested over a 6-week class with about 10 students and can be used as an educational tool.en_US
dc.description.statementofresponsibilityby Kevin Zhu.en_US
dc.format.extent82 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleAn educational approach to machine learning with mobile applicationsen_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.identifier.oclc1127289989en_US
dc.description.collectionM.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienceen_US
dspace.imported2019-11-22T00:00:18Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentEECSen_US


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