Widening Access to Applied Machine Learning with TinyML

@article{Reddi2022WideningAT,
  title={Widening Access to Applied Machine Learning with TinyML},
  author={Vijay Janapa Reddi and Brian Plancher and Susan Kennedy and Laurence Moroney and Pete Warden and Anant Agarwal and Colby R. Banbury and Massimo Banzi and Matthew Bennett and Benjamin Brown and Sharad Chitlangia and Radhika Ghosal and Sarah Grafman and Rupert Jaeger and Srivatsan Krishnan and Maximilian Lam and Daniel Leiker and Cara Mann and Mark Mazumder and Dominic Pajak and Dhilan Ramaprasad and J. Evan Smith and Matthew P. Stewart and Dustin Tingley},
  journal={ArXiv},
  year={2022},
  volume={abs/2106.04008}
}
Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this paper, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, ML on resource-constrained embedded devices, is an attractive means to widen access… 
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