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On-Device Training of Machine Learning Models with MATLAB and Simulink


This webinar demonstrates how to use MATLAB and Simulink to create machine learning models that can be trained on an embedded device to adapt to new data.  Attendees will learn:

  • The basics of on-device learning techniques and typical applications and devices
  • Motivation for training machine learning models on embedded devices
  • Challenges involved in on-device learning
  • Two main approaches to on-device learning: using passive or active model updates
  • The steps in a typical workflow using an audio classification example

Please allow approximately 45 minutes to attend the presentation and Q&A session. We will be recording this webinar, so if you can't make it for the live broadcast, register and we will send you a link to watch it on-demand.

About the Presenters

Jack Ferrari is a product manager at MathWorks, focused on code generation for deep learning models. He is also the product manager for Deep Learning Toolbox Model Quantization Library, enabling MATLAB users to compress and deploy AI models to edge devices and embedded systems. Jack holds a B.S. in Mechanical Engineering from Boston University.

Brad Duncan is the product manager for machine learning capabilities in the Statistics and Machine Learning Toolbox at MathWorks.  He works with customers to apply AI in new areas of engineering such as incorporating virtual sensors in engineered systems, building explainable machine learning models, and standardizing AI workflows using MATLAB and Simulink.  

Product Focus

On-Device Training of Machine Learning Models with MATLAB and Simulink

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