Skills you'll gain

There are 6 modules in this course

Teaching AI on the Edge is designed to equip educators and learners with the knowledge and skills to successfully implement artificial intelligence in resource-constrained environments. This course blends essential theoretical foundations with practical project-based experiences, preparing you to understand, build, and effectively teach AI systems optimized for edge devices. You'll explore the evolution from specialized task-specific AI models to versatile multimodal foundation models, learning critical techniques such as pruning, quantization, and small-model design that allow advanced AI capabilities to operate efficiently on limited hardware. The course emphasizes iterative development practices, rigorous model evaluation, and responsible AI deployment, highlighting data privacy, model bias, and regulatory considerations. Throughout this course, you'll gain insights into practical teaching strategies that balance theory and hands-on activities, encouraging creative, inclusive, and collaborative approaches to AI education. You'll also discover how to leverage open-source tools and frameworks to accelerate learning and inspire students to tackle real-world problems through innovative edge AI solutions. Join us to deepen your understanding of AI's potential, master effective teaching practices, and inspire the next generation of AI innovators to positively impact society.

Ways of Teaching AI on the Edge

Technical Considerations when Teaching AI on the Edge

Considering Domain and Applications when Teaching AI on the Edge

Teaching the Societal Impact and Ethics of AI

Closing Material

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