Tutorial on Network Intelligence: machine learning for Networks
Network Intelligence course encompasses three main parts. In the first part of the course, we aim to present the background on Network Intelligence i.e. the latest state of the art on Artificial Intelligence (AI) for network and service management. In the second part of the course, we will delve into Machine Learning (ML) for network data analysis. We will be introducing and discussing various techniques for specific problems (prediction, classification, clustering, etc.), as well as relevant pointers to consider in the domain. In the third part, we will present a hands-on demonstration through a notebook capturing the end-to-end process of leveraging ML in a network management use case (forecasting of network data, anomaly detection, etc.). The demonstration will make use of real network data.
The participants will be presented with:
- An overview of past literature works and ongoing efforts on applying ML for network operations and management.
- A concise course on applying ML for network data analytics with relevant pointers and useful takeaways.
- Insights about best environments to use (opensource libraries and frameworks)
- A hands-on demonstration showcasing ML techniques applied to a network management use case scenario that involves real network traces.
Teaching and hands-on experience of the instructors will be leveraged for best possible outcome.
Speakers: Imen Grida Ben Yahia, Bruno Kauffmann, Noura Limam; Orange, France and University of Waterloo, Canada