Historically, computing instructors and researchers have developed a wide variety of tools to support teaching and educational research, including exam and code testing suites and data collection solutions. Many are then community or individually maintained. However, these tools often find limited adoption beyond their creators. As a result, it is common for many of the same functionalities to be re-implemented by different instructional groups within the CS Education community. We hypothesize that this is due in part to accessibility, discoverability, and adaptability challenges, among others. Further, instructors often face institutional barriers to deployment, which can include hesitance of institutions to utilize community developed solutions that often lack a centralized authority. This working group will explore what solutions are currently available, what instructors need, and reasons behind the above-mentioned phenomenon.
EASI-22 was a 3-day, hands-on workshop for high school teachers and students exploring real-world applications of artificial intelligence at the edge through hands-on examples of Tiny Machine Learning (TinyML). This program was a collaboration between Navajo Technical University, the Harvard John A. Paulson School of Engineering and Applied Sciences, and Barnard College, Columbia University.
SciTinyML-22 was a, five day, hands-on, virtual workshop exploring how embedded ML (tinyML) can impact the developing world through hands-on activities using embedded hardware devices. SciTinyML-22 was run regionally with seperate workshops for Africa (187 participants from 29 countries), Asia, and Latin America. This program was a collaboration led by the Abdus Salam International Centre for Theoretical Physics (ICTP), the Harvard John A. Paulson School of Engineering and Applied Sciences, and TinyMLedu.
[TinyMLedu](https://tinymledu.org) is working to build an international coalition of researchers and practitioners advancing TinyML in the developing world, and to develop and share high-quality, open-access educational materials globally.
In this paper, we describe our pedagogical approach to increasing access to applied ML through a four part massive open online course (MOOC) on Tiny Machine Learning (TinyML) produced in collaboration between academia (Harvard University) and industry (Google). We suggest that TinyML, ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML both leverages low-cost and globally accessible hardware, and encourages the development of complete, self-contained applications, from data collection to deployment. We also released the course materials publicly, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies.
SciTinyML-21 was a, five day, hands-on, virtual, global (216 participants from 48 countries) workshop exploring how embedded ML (tinyML) can impact the developing world through hands-on activities using the Edge Impulse cloud platform and a smartphone. This program was a collaboration between the Abdus Salam International Centre for Theoretical Physics (ICTP), the Harvard John A. Paulson School of Engineering and Applied Sciences, and TinyML4D.
CRESTLEX 3.0 was a first-of-its-kind, 4-day, hands-on workshop for high school teachers and students exploring real-world applications of artificial intelligence through hands-on examples of Tiny Machine Learning (TinyML). This program was a collaboration between Navajo Technical University, the Harvard John A. Paulson School of Engineering and Applied Sciences, Google, and Edge Impulse.
In this exciting Professional Certificate program offered by Harvard University and Google TensorFlow, you will learn about the emerging field of Tiny Machine Learning (TinyML), its real-world applications, and the future possibilities of this transformative technology. TinyML is a cutting-edge field that brings the transformative power of machine learning (ML) to the performance-constrained and power-constrained domain of embedded systems. The program will emphasize hands-on experience and is a collaboration between expert faculty at Harvard’s John A. Paulson School of Engineering and Applied Sciences (SEAS) and innovative members of Google’s TensorFlow team.
We describe the pedagogy behind the MIT Beaver Works Summer Institute Robotics Program, a new high-school STEM program in robotics. The program utilizes state-of-the-art sensors and embedded computers for mobile robotics. The program was offered as a four-week residential program at MIT in the summer of 2016.
Driverless vehicle technology has been growing at an exponential pace since the DARPA Grand and Urban Challenges pushed the state of the art to demonstrate what was already possible. Commercial interest and aggressive development are being driven by Google, Toyota, Tesla, Continental, Uber, Apple, NVidia, and many other companies. There is no single technology or “killer app” available to solve the myriad perception, understanding, localization, planning, and control problems required to achieve robust navigation in highly variable, extremely complex and dynamically changing environments. This summer, Beaver Works Summer Institute will offer nine teams of five students, each with its own MIT-designed RACECAR (Rapid Autonomous Complex Environment Competing Ackermann steeRing) robot, the opportunity to explore the broad spectrum of research in these areas, learn to collaborate, and demonstrate fast, autonomous navigation in a Mini Grand Prix to Move... Explore... Learn...Race!