Teaching AI on the Edge

Overview

Mobile and edge devices are revolutionizing artificial intelligence (AI) applications by deploying large language models (LLMs) capable of transforming society. This course addresses how academia can prepare the upcoming generation of engineers to leverage these opportunities and tackle the challenges associated with AI on the Edge.

Join Dr.

Catherine Breslin, an AI consultant and co-founder of Kingfisher Labs, as she delves into the critical considerations for teaching AI within higher education. "Teaching AI on the Edge" aims to provide educators and learners with essential knowledge and skills to implement AI effectively in environments with limited resources.

Participants will learn to blend foundational theories with practical project-based experiences, enabling them to understand, build, and teach AI systems optimized for edge devices. The course covers the transition from task-specific AI models to multimodal foundation models, focusing on techniques like pruning, quantization, and designing smaller models for efficient operation on constrained hardware.

Emphasizing iterative development, rigorous model evaluation, and responsible AI deployment, the course highlights essential issues such as data privacy, model bias, and regulatory factors.

Gain insights into teaching strategies that balance theoretical content with engaging, hands-on activities, fostering creative and collaborative learning experiences.

Additionally, discover how to utilize open-source tools and frameworks to speed up learning and inspire students to apply innovative edge AI solutions to real-world challenges. By the end of the course, you will have a deeper understanding of AI's potential, master impactful teaching methods, and inspire future AI pioneers to create positive societal changes.

Provider:

Coursera

Categories:

Artificial Intelligence Courses, Machine Learning Courses, Foundation Models Courses, Multimodal AI Courses, Model Optimization Courses

Subjects