Empowering novices to understand and use machine learning with personalized image classification models, intuitive analysis tools, and MIT App Inventor
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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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2019
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| Online Access: | https://hdl.handle.net/1721.1/123130 |
| _version_ | 1826216934180388864 |
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| author | Tang, Danny,M. Eng.Massachusetts Institute of Technology. |
| author2 | Harold Abelson. |
| author_facet | Harold Abelson. Tang, Danny,M. Eng.Massachusetts Institute of Technology. |
| author_sort | Tang, Danny,M. Eng.Massachusetts Institute of Technology. |
| 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 | 2024-09-23T16:55:28Z |
| format | Thesis |
| id | mit-1721.1/123130 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T16:55:28Z |
| publishDate | 2019 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1231302019-12-05T18:05:07Z Empowering novices to understand and use machine learning with personalized image classification models, intuitive analysis tools, and MIT App Inventor Tang, Danny,M. Eng.Massachusetts Institute of Technology. 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 129-131). As machine learning permeates our society and manifests itself through commonplace technologies such as autonomous vehicles, facial recognition, and online store recommendations, it is necessary that the increasing number of people who rely on these tools understand how they work. As such, we need to develop effective tools and curricula for introducing machine learning to novices. My work focuses on teaching core machine learning concepts with image classification, one of the most basic and widespread examples of machine learning. I built a web interface that allows users to train and test personalized image classification models on pictures taken with their computers--webcams. Furthermore, I built an extension for MIT App Inventor, a platform for building mobile applications using a blocks-based programming language, that allows users to use the models they built in the web interface to classify objects in their mobile applications. Finally, I created high school level curricula for workshops based on using the aforementioned interface and App Inventor extension, and ran the workshops with two classes of high school students from Boston Latin Academy. My findings indicate that high school students with no machine learning background are able to learn and understand general concepts and applications of machine learning through hands-on, non-technical activities, as well as successfully utilize models they built for personal use. by Danny Tang. M. Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 2019-12-05T18:05:06Z 2019-12-05T18:05:06Z 2019 2019 Thesis https://hdl.handle.net/1721.1/123130 1128813816 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 131 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Tang, Danny,M. Eng.Massachusetts Institute of Technology. Empowering novices to understand and use machine learning with personalized image classification models, intuitive analysis tools, and MIT App Inventor |
| title | Empowering novices to understand and use machine learning with personalized image classification models, intuitive analysis tools, and MIT App Inventor |
| title_full | Empowering novices to understand and use machine learning with personalized image classification models, intuitive analysis tools, and MIT App Inventor |
| title_fullStr | Empowering novices to understand and use machine learning with personalized image classification models, intuitive analysis tools, and MIT App Inventor |
| title_full_unstemmed | Empowering novices to understand and use machine learning with personalized image classification models, intuitive analysis tools, and MIT App Inventor |
| title_short | Empowering novices to understand and use machine learning with personalized image classification models, intuitive analysis tools, and MIT App Inventor |
| title_sort | empowering novices to understand and use machine learning with personalized image classification models intuitive analysis tools and mit app inventor |
| topic | Electrical Engineering and Computer Science. |
| url | https://hdl.handle.net/1721.1/123130 |
| work_keys_str_mv | AT tangdannymengmassachusettsinstituteoftechnology empoweringnovicestounderstandandusemachinelearningwithpersonalizedimageclassificationmodelsintuitiveanalysistoolsandmitappinventor |