Units
2.0 QUARTER UNITS
Course Description
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 AI hardware based on OpenML. 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 OpenML.
- Train and optimize models for embedded systems, focusing on performance and efficiency.
- Develop a capstone project that integrates AI into a practical embedded solution.
Prerequisites / Skills Needed
You will need programming experience in Python.
Additional Information
AI* - This course introduces the foundational structures of modern AI models and guides students through hands-on creation, training, and deployment of machine-learning systems on embedded hardware.
Currently no classes scheduled. Would you like to be notified when a class is available?
This course applies to these programs:
