@article {677215,
	title = {Widening Access to Applied Machine Learning with TinyML},
	journal = {Harvard Data Science Review},
	volume = {4},
	number = {1},
	year = {2022},
	abstract = {Broadening access to both computational and educational resources is criticalto diffusing machine-learning (ML) innovation. However, today, most ML resources\&nbsp;and experts are siloed in a few countries and organizations. In this paper, we describe\&nbsp;our pedagogical approach to increasing access to applied ML through a massive open\&nbsp;online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML,\&nbsp;applied ML on resource-constrained embedded devices, is an attractive means to widen\&nbsp;access because TinyML both leverages low-cost and globally accessible hardware, and\&nbsp;encourages the development of complete, self-contained applications, from data collection\&nbsp;to deployment. To this end, a collaboration between academia and industry produced a\&nbsp;four-part MOOC that provides application-oriented instruction on how to develop solutions\&nbsp;using TinyML. The series is openly available on the edX MOOC platform, has no\&nbsp;prerequisites beyond basic programming, and is designed for global learners from a variety\&nbsp;of backgrounds. It introduces pupils to real-world applications, ML algorithms, data-set\&nbsp;engineering, and the ethical considerations of these technologies through hands-on programming\&nbsp;and deployment of TinyML applications in both the cloud and on their own\&nbsp;microcontrollers. To facilitate continued learning, community building, and collaboration\&nbsp;beyond the courses, we launched a standalone website, a forum, a chat, and an optional\&nbsp;course-project competition. We also open-sourced the course materials, hoping they will\&nbsp;inspire the next generation of ML practitioners and educators and further broaden access\&nbsp;to cutting-edge ML technologies.},
	url = {https://hdsr.mitpress.mit.edu/pub/0gbwdele/release/3},
	author = {Vijay Reddi and Brian Plancher and Susan Kennedy and Laurence Moroney and Pete Warden and ManyGreatPeopleReadThePaper and Tingley, Dustin}
}
