Edge AI is revolutionizing embedded systems by bringing powerful machine learning capabilities to low-power devices. This hands-on course explores how AI can be deployed for visual AI (object detection with a camera), audio AI (keyword and command detection), and even lightweight large language models (LLMs) for natural language processing.

Designed for aspiring engineers and developers, students will explore how to leverage AI to enhance the capabilities of embedded hardware like low-power MCU-based systems on a chip ESP32-S3 by Expressif. Over eight sessions, participants will learn the fundamentals of AI, build and optimize neural networks, and deploy custom AI models for real-world applications.

With a strong emphasis on practical application, this course blends in-class exercises, homework assignments, and project-based learning to ensure a deep understanding of AI's potential in embedded systems. Whether you're new to AI or looking to enhance your expertise, this course offers the tools and knowledge to innovate in the rapidly growing field of embedded AI.

Learning Outcomes
At the conclusion of the course, you should be able to:

• Explain fundamental AI concepts and neural network principles.
• Implement the AI applications using tools like TensorFlow Lite and Edge Impulse on OpenMV.
• Train and optimize models for embedded systems, focusing on performance and efficiency.
• Develop a capstone project that integrates AI into a practical embedded solution.

Skills Needed:

You will need programming experience in Python.