An educational approach to machine learning with mobile applications
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
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| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2019
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| Online Access: | https://hdl.handle.net/1721.1/122989 |
| _version_ | 1826217543521533952 |
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| author | Zhu, Kevin(Kevin F.) |
| author2 | Harold Abelson. |
| author_facet | Harold Abelson. Zhu, Kevin(Kevin F.) |
| author_sort | Zhu, Kevin(Kevin F.) |
| collection | MIT |
| description | This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. |
| first_indexed | 2024-09-23T17:05:20Z |
| format | Thesis |
| id | mit-1721.1/122989 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T17:05:20Z |
| publishDate | 2019 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1229892019-11-22T03:00:54Z An educational approach to machine learning with mobile applications Zhu, Kevin(Kevin F.) Harold Abelson. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Electrical Engineering and Computer Science. 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 2019-11-22T00:00:21Z 2019-11-22T00:00:21Z 2019 2019 Thesis https://hdl.handle.net/1721.1/122989 1127289989 eng 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 82 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Zhu, Kevin(Kevin F.) An educational approach to machine learning with mobile applications |
| title | An educational approach to machine learning with mobile applications |
| title_full | An educational approach to machine learning with mobile applications |
| title_fullStr | An educational approach to machine learning with mobile applications |
| title_full_unstemmed | An educational approach to machine learning with mobile applications |
| title_short | An educational approach to machine learning with mobile applications |
| title_sort | educational approach to machine learning with mobile applications |
| topic | Electrical Engineering and Computer Science. |
| url | https://hdl.handle.net/1721.1/122989 |
| work_keys_str_mv | AT zhukevinkevinf aneducationalapproachtomachinelearningwithmobileapplications AT zhukevinkevinf educationalapproachtomachinelearningwithmobileapplications |