How to build decision trees in sensors with Machine Learning to create power-efficient AI applications for edge computing solutions
During this one-hour webinar, you will learn how to run a classification engine on the Machine Learning Core embedded in our latest iNEMO™ inertial modules, based on a decision-tree logic. In this webinar we will show you how to quickly and easily design power-efficient decision trees using the AlgoBuilder Graphical User Interface and ensure they provide accurate results in the shortest possible time.
From theory to practice, we will implement the ready-to-go IoT node SensorTile.box, together with AlgoBuilder and Unico GUIs in a practical MLC example, without having to write a single line of code.
Discover the power of Machine Learning and join us to learn how ST’s MLC solutions, ecosystem and Graphical User Interfaces can support AI application development and reduce your time-to-market.
You will learn
- How to program a sensor with a Machine Learning Core without writing a single line of code
- The key 5 steps behind sensors Machine Learning with a practical example
- How to use and benefit AlgoBuilder and Unico GUIs
- About the features and benefits of using the ready-to-go SensorTile.box kit
- Introduction to Machine Learning in Motion MEMs
- Machine Learning Core and ecosystem
- Decision Tree design with AlgoBuilder and Unico-GUI
- MLC practical example