The Tiny Machine Learning Open Education Initiative (TinyMLx)
The Tiny Machine Learning Open Education Initiative (TinyMLx)
Project Overview
TinyML is a cutting-edge field that brings the transformative power of machine learning (ML) to the performance- and power-constrained domain of embedded systems. Successful deployment in this field requires intimate knowledge of applications, algorithms, hardware, and software. We are working to improve global access to TinyML educational materials through our MOOC courses, the TinyML4D Academic Network, and a variety of other projects.
To learn more about the initiative visit our website https://tinymlx.org!
Publications
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[Preprint - Under Review] (2021). Widening Access to Applied Machine Learning with TinyML.
PDF Course Main Page TinyML Open Education Initiative MOOC Website MOOC Course Materials
Courses
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CRESTLEX 3.0 - CReating Effective STem Learning EXperiences
Summer 2021 Website
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HarvardX: Tiny Machine Learning MOOC
Launched Fall 2020 Course White Paper MOOC Website Course Materials TinyML Open Education Initiative Discourse Forum
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Harvard CS249r: Tiny Machine Learning (TinyML)
Fall 2020 Syllabus Website Select Lecture Videos Assignments Project Instructions
Collaborators
- Anant Agarwal
- Benjamin Brown
- Cara Mann
- Colby Banbury
- Daniel Leiker
- Dhilan Ramaprasad
- Dominic Pajak
- Dustin Tingley
- Gregg Barrett
- Hal Speed
- J. Even Smith
- Laurence Moroney
- Marcelo Rovai
- Marco Zennaro
- Mark Mazumder
- Massimo Banzi
- Matthew Bennett
- Matthew Stewart
- Maximilian Lam
- Pete Warden
- Radhika Ghosal
- Rupert Jaeger
- Sarad Chitlangia
- Sarah Grafman
- Srivatsan Krishnan
- Susan Kennedy
- Vijay Janapa Reddi
