• Technical Conference:  30 March – 03 April 2025
  • Exhibition: 01 – 03 April 2025
  • Moscone Center, San Francisco, California, USA

Short Courses

SC483 - Machine Learning in Optical Networks

Monday, 31 March
08:30 - 12:30 (Pacific Time (US & Canada), UTC - 08:00)

Short Course Level: Beginner

Instructor:

Massimo Tornatore, Politecnico di Milano, Italy
 

This short course will be held in person only. Please check your email for information on the location where this short course will be held. If you need assistance please visit the Info Desk by registration.
Short Course Description:

Machine learning (ML) has recently attracted a surge of interest in optical networking and communication research due to its pattern recognition and predictive capabilities for various key applications. Large-scale monitoring data are generated every day in optical networks, which makes ML a promising solution for decision making. In this short course, we introduce the fundamental concepts and principles of ML, with a special focus on multi-layer neural networks. We survey existing work on various applications in optical domain, ranging from fault management, quality of transmission estimation and low-margin network design. We aim to provide a general overview of the key problems, common formulations, existing methodologies and future directions. This course will inspire the audience and facilitate ML research and development in optical networking and communication systems.

The outline is given below,

  • Fundamental concepts of ML
  • ML model learning and evaluation
  • Neural Networks fundamentals
  • ML Applications
    • Quality-of-transmission estimation
    • Low-margin network design
    • Failure detection and identification
    • Other applications
  • Hands-on lab activity
    • All attendees should bring laptops to participate in the hands-on software lab. We suggest having Python installed on your machine.
Short Course Benefits:

This course should enable participants to, 

  • Identify the main machine learning categories
  • Define with the most relevant ML algorithms used in practice (with a focus on neural networks)
  • Discuss the current status of the ML technology and its applications in optical networking and communications
  • Compare properties and requirements for various ML evaluation techniques
  • Implement a proof-of concept ML algorithm for QoT estimation
Short Course Audience:

The course is intended for interested people from academia and industry without any previous knowledge in machine learning. A basic understanding of optical fiber transmission and programming can be helpful but is not a hard requirement. Attendance is also beneficial for machine learning experts with limited optical networking background who want to learn about the potential applications in the area of optical communication and networking.

Instructor Biography:

Massimo Tornatore 

is a Professor at Politecnico di Milano, Italy. He has also held appointments as Adjunct Professor at University of California, Davis, USA and as visiting professor at University of Waterloo, Canada. His research interests include performance evaluation and design of communication networks (with an emphasis on optical networking), and machine learning application for network management. He co-authored more than 500 conference and journal papers (with 21 best-paper awards) and of the recent Springer “Handbook of Optical Networks”.  He is member of the Editorial Board of IEEE Communication Surveys and Tutorials, IEEE Transactions on Network and Service Management, IEEE Transactions on Networking, and Elsevier Optical Switching and Networking. He is a fellow of the IEEE.