This workshop is organized by Olga Saukh.
On invitation only.
Machine learning is the key enabling technology for many Internet of Things applications. However, making machine learning models operate reliably in the physical world faces unique challenges.
Collection and processing of sensor data to build an accurate machine learning model is difficult for embedded devices due to their resource constraints, privacy threats and bandwidth limitations. The challenge gets even more severe if the data is noisy and decentralized. Machine learning also plays an important role in adapting the parameters of the deployed networked embedded devices to environmental dynamics.
This workshop focuses on how to address all these challenges and bring intelligence to networked embedded systems. We will discuss the current status of the ongoing research projects in this area and plan collaborations across research groups co-affiliated with TU Graz.