Abstract
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 article, 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, applied ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML leverages low-cost and globally accessible hardware and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia and industry produced a four part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for global learners from a variety of backgrounds. It introduces real-world applications, ML algorithms, data-set engineering, and the ethical considerations of these technologies through hands-on programming and deployment of TinyML applications in both the cloud and on their own microcontrollers. To facilitate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project competition. We also open-sourced the course materials, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies.
Highlights
The past two decades have seen machine learning (ML) progress dramatically from a purely academic discipline to a widespread commercial technology that serves a range of sectors
The course is motivated by realworld applications, covering the software and hardware and the product life cycle and responsible AI considerations needed to deploy these applications. To make it globally accessible and scalable, we focused on the emerging Tiny Machine Learning (TinyML) domain and released the course as a massive open online course (MOOC) on edX
It starkly contrasts with traditional ML, which increasingly focuses on large-scale implementations that are often confined to the cloud
Summary
The past two decades have seen machine learning (ML) progress dramatically from a purely academic discipline to a widespread commercial technology that serves a range of sectors. Developed as an academic and industry collaboration, the resulting massive open online course (MOOC), TinyML on edX, teaches hands-on applied ML through the lens of real-world Tiny Machine Learning (TinyML) applications, and considers the ethical and life-cycle challenges of industrial product development and deployment (see Figure 1). Understanding ethical reasoning is a crucial skill for ML engineers as inaccurate or unpredictable model performance can erode consumer trust and reduce the chance of success To this end, we collaborated with the Harvard Embedded EthiCS program to integrate a responsible-AI curriculum into each course, providing opportunities to practice identifying ethical challenges and thinking through potential solutions to concrete problems based on real-world case studies. The remainder of the sections, which outline future initiatives, demographic information associated with course participants, limitations, and related work, are broadly applicable to all of the above audiences
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