On-demand Webinar | Program decision trees in sensors with a Machine Learning Core

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

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Lisa Trollo

Lisa is specialized in Artificial Intelligence applications for MEMS and sensors. After joining ST in 2012, she actively supported the development of ST’s innovations in MEMS and sensors and explored new market opportunities. 

Lisa has significant experience in product marketing and promotion and has played a key role in many success stories in consumer, automotive and industrial markets.

Denis Ciocca

Denis is Staff Applications Engineer at STMicroelectronics specializing in Smart Sensors, Linux device driver and Android OS. He has developed a variety of solutions with MEMS sensors, a computational platform of STM32 microcontrollers and wireless connectivity solutions. Denis has received his Master’s degree in Computer Science and Engineering from the University of Pavia, Italy.