Empowering novices to understand and use machine learning with personalized image classification models, intuitive analysis tools, and MIT App Inventor
Author(s)Tang, Danny,M. Eng.Massachusetts Institute of Technology.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
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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.
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, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 129-131).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.