Overview

Not all machine learning is deployed to large clusters; many of our everyday devices like smartwatches and smart thermostats run ML models on low-power embedded computers. The Graduate Certificate in Tiny Machine Learning prepares students to develop and deploy machine learning models for use on embedded systems that use data form on-board sensors and other sources. Students consider tradeoffs in model accuracy, computational power, and energy usage along with security and reliability concerns.

 All 8 credits can be applied towards the Master's in Machine Learning and can be completed in two semesters.

If you’re looking for a program that leverages cutting-edge AI and analytics without the heavy math prerequisites, consider the Graduate Certificate in Advanced Business Strategy Using AI and Analytics offered by the Rader School of Business. This program is designed to equip you with the strategic insights and analytical skills needed to excel in today’s data-driven business environment.

Why Obtain a Graduate Certificate in Tiny Machine Learning?

Students who complete this certificate will be well positioned for industry roles related to TinyML and Internet of Things. Local companies are increasingly using machine learning to replace hand-written algorithms for embedded systems that control appliances, heavy equipment, and more.  They are eager to hire people who have experience with developing ML solutions that work on these resource-constrained platforms and with the type of data these platforms often encounter.

Further, manufacturing companies are increasingly using sensors anad other real-time information to monitor and guide maintenance and quality control.  By replacing fixed maintenance schedules with data-driven approaches for scheduling maintenance as needed, companies are increasing safety for their workers, reducing costs, and reducing production delays.  These group of approaches are often referred to as the Industrial Internet of Things (IIoT).  The TinyML certificate teaches students about the sensor data, embedded systems,  and machine learning algorithms that form the technological core of IIoT.

Admissions Process

1. Submit application online here.
2. Submit supporting documents to admissions counselor via email.
3. Completed file reviewed by program director for admission decision.
4. Upon acceptance, submit your enrollment deposit to secure your spot in the program.
5. Get access to your myMSOE account and MSOE email.
6. Admissions registers you for appropriate coursework and sends schedule when finalized by the Registrar’s office.
7. You are officially an MSOE Raider!