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Teach edge AI learning using Edge Impulse

The Edge Impulse University Program empowers educators from around the world to train the next generation of edge machine learning professionals. Edge Impulse Studio is a great tool for teaching as it removes the complexity of ML tooling while still providing the depth and explainability required for learning. Our platform is used across industry and provides a strong foundation of easy to learn tools for anyone starting their career in embedded systems and AI.

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Enterprise Access for Educators

Manage classes with more collaboration, project management, unrestricted job time and more!

Learning resources

Access to lesson plans, and sample projects, our textbook on AI at the Edge

Community support

Join our global community of educators inspiring the next generation of engineers. Connect with peers through our Discord channel of edge ML experts and educators or on the Forum..

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What you get

An Edge Impulse Organization for your course making easy to manage coursework and teaching:

Easily invite new students

Manage project access and easily keep track of project progress

Provide custom blocks for students (e.g. deployment blocks for your own teaching hardware)

Share external API keys safely (e.g. OpenAI, Google Gemini) for use with Synthetic Data and Labelling blocks

Access to enterprise features in your projects

No restriction on job length (with a generous organization-wide compute allocation)

Access for one year (if you’re running a course next year we can renew your access)

How to join

Check eligibility

This program is open to educators teaching students aged 18 and above as part of a degree or diploma-granting course such as university, college, or technical institute worldwide.

Create an account

Create an account with your institutional email (e.g., “.edu”, “.ac.uk”) using the following link.

Request access to the program

Complete the application form to request access to the program. We’ll need a few details about your class size, course and institution to accept your application. If you don’t need or aren’t eligible for Enterprise Access you can still apply here to stay up to date with any future opportunities for educators.

Our team will review your application. You should get an answer within a few days.

Edge AI Courseware

We have built two high quality courses that provide a comprehensive collection of slides, reading material, videos, and project prompts organized into modules. You are welcome to download, modify, and integrate this material into your curriculum or share the complete online course with your students for extracurricular learning!

“Edge Impulse courseware interface titled ‘courseware‑embedded‑machine‑learning.’ It shows public modules for teaching, including ‘Module 1 – Introduction to Machine Learning’ and ‘Module 2 – Getting Started with Deep Learning,’ each marked as updated 3 days ago—providing downloadable slides, readings, videos, and project prompts for embedded ML education.”

Introduction to Embedded Machine Learning

Learn the fundamentals of machine learning as well as how to build and deploy models on microcontrollers. This course covers core ML concepts, training neural networks, and hands-on projects in embedded ML. Microcontrollers are commonly used in mass market deployments of edge AI due to their low cost and power requirements, learn about how to deal with these constraints to build successful use-cases with edge AI. No prior ML experience is required, but basic familiarity with Arduino, microcontrollers, and maths is recommended.

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Edge AI Development on Linux Platforms

This course introduces developers to Edge AI using Edge Impulse on Linux-based platforms. It covers Edge AI fundamentals, the benefits of Linux for edge computing, and practical applications using a range of sensors from microphones to cameras.. This course covers deployment on linux targets such as single board computers which are common in higher end industrial use-cases. A basic familiarity with python, linux and maths is recommended.

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AI at the Edge

This textbook provides a great high-level overview on the challenges and advantages of implementing AI at the Edge, as well as a large number of practical examples. It is an end-to-end framework for solving real-world industrial, commercial, and scientific problems with edge Al. You'll explore every stage of the process, from data collection to model optimization to tuning and testing, as you learn how to design and support edge Al and embedded ML products.

Download a free copy

Academic Paper

Edge Impulse partnered with Harvard SEAS to coauthor an academic paper detailing how Edge Impulse provides solutions to many of the challenges faced by the edge and embedded machine learning communities. You can read the abstract and full paper on arXiv.

See paper

Edge Impulse Studio comes to our rescue, helping the Electronics, Computing, and Control and Automation students to abstract frameworks, such as TensorFlow, to gather data and deploy trained models into embedded devices. Edge Impulse supported our students in all steps of their edge machine learning projects.

Prof. Marcelo José Rovai

Universidade Federal de Itajubá

The future of machine learning is tiny and bright. Edge impulse makes it easy to onboard learners and helps them get started with embedded ML

Prof. Vijay Janapa Reddi

Harvard University

Edge Impulse makes it incredibly easy to get started with machine learning and AI. I especially appreciate their commitment to engaging folks in industry, entrepreneurs, hobbyists, students, educators, researchers, and the like. Making their system accessible for the masses is what really impresses me the most about them.

Orlando S. Hoilett, Ph.D.

Purdue University

Having access to Edge Impulse was huge--it really helped my students better understand the big picture use case for many of the ML techniques we were studying.

LTC David M. Feinauer, PhD, PE

Virginia Military Institute

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If you have any questions about the Edge Impulse University Program, please do not hesitate to contact us.

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“Edge Impulse getting started graphic: three connected steps —‘Integrate with your workflow’ with an image of hands typing on a keyboard, ‘Unlock sensor data value’ with an image of a microcontroller board, and ‘Optimize AI for any hardware’ with an image of industrial machines.”

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