Empowering novices to understand and use machine learning with personalized image classification models, intuitive analysis tools, and MIT App Inventor

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Tang, Danny,M. Eng.Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Harold Abelson.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/123130

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123130