TinyML: Edge & Embedded
HarvardX Professional Certificate in Tiny Machine Learning
The complete courseware from the HarvardX TinyML Professional Certificate on edX — 4 courses across 5 chapters covering ML fundamentals through embedded deployment and MLOps. 178 slide decks, 127 readings, and 23 supplementary materials. Originally developed by Harvard SEAS and Google TensorFlow.
Fundamentals of TinyML
ML basics, deep learning building blocks, CNNs, computer vision, and responsible AI design.
Course 2Applications of TinyML
TensorFlow Lite, quantization, keyword spotting, visual wake words, anomaly detection, and data engineering.
Course 3Deploying TinyML
Embedded hardware, TFLite Micro internals, and hands-on deployment of KWS, VWW, and gesture recognition on Arduino.
Course 4MLOps for Scaling TinyML
ML development lifecycle, continuous training, model conversion, deployment at scale, prediction serving, and monitoring.
| TinyML Syllabus | 10–12 week semester plan with weekly assignments, learning objectives, and adaptation guides. | Syllabus → |
| Hardware Kits | Arduino, Raspberry Pi, and Seeed deployment labs for hands-on TinyML projects. | Kits → |
| Textbook | Volume I covers the systems foundations: compression, hardware acceleration, benchmarking, and deployment. | Vol I → |
| edX Certificate | Self-paced online version of this curriculum on the HarvardX edX platform. | edX → |
The TinyMLx Team
Instructors: Vijay Janapa Reddi, Laurence Moroney, Pete Warden, Lara Suzuki
Guest Instructor: Susan Kennedy
Staff Lead: Brian Plancher
Staff: Colby Banbury, Benjamin Brown, Dhilan Ramaprasad, J. Evan Smith, Matthew Stewart
Contributors: Sharad Chitlangia, Radhika Ghosal, Srivatsan Krishnan, Maximilian Lam, Mark Mazumder
These materials were originally developed for the HarvardX Professional Certificate in Tiny Machine Learning on edX. See the original curriculum for the full item-by-item breakdown including forum prompts and quizzes not listed above.