2026-09-20T18:45:32Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1229892026-06-06T00:56:25Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Harold Abelson.
Zhu, Kevin(Kevin F.)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2019-11-22T00:00:21Z
2019-11-22T00:00:21Z
2019
2019
https://hdl.handle.net/1721.1/122989
1127289989
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 81-82).
Machine 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.
by Kevin Zhu.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
82 pages
eng
Massachusetts Institute of Technology
MIT 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.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
An educational approach to machine learning with mobile applications
Thesis
application/pdf
2019-11-22T00:00:18Z
Publication
Master
EECS
unknown
unknown
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