LSACROFT Project Student Lecture 1: TinyML with TensorFlow Lite on Ultra Low Power Microcontrollers
This video presents a student-led seminar on TinyML, focusing on practical machine learning implementation on ultra-low-power microcontrollers. The seminar is based on the book TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra‑Low‑Power Microcontrollers by Pete Warden and Daniel Situnayake.
The seminar covers Chapters 1–12 of the book, introducing the fundamental concepts of Tiny Machine Learning (TinyML) and demonstrating how machine learning models can run on small embedded devices such as microcontrollers.
The session includes:
1. Introduction to TinyML and its role in edge AI and embedded systems
2. Fundamentals of machine learning for microcontrollers
3. Data collection and preprocessing for embedded ML applications
4. Building, training, and optimizing lightweight machine learning models
5. Deploying models using TensorFlow Lite on Arduino and ultra-low-power devices
6. Practical applications of TinyML in sensing, IoT, and environmental monitoring
The session was presented by Ralph Tetteh, Elijah Nansuuri, and Sarah Annang and held on 13th March 2026 at CSIR–INSTI.
This seminar is part of the student capacity-building activities of the LSACROFT Project, which aims to strengthen practical skills in embedded AI, low-power sensing technologies, and intelligent data processing for applications in agriculture, environmental monitoring, and smart systems.
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