Machine Learning for Computer Networking
Dienstag, 14:15 – 15:45 Uhr, Raum 04.137 UnivIS
Seminar 2 SWS
Artificial intelligence and machine learning (ML) have shown amazing success in various application fields like for instance speech recognition, computer vision and data analytics in general. Popular applications use artificial neural networks, Markov models, Bayesian networks and similar methods. Recently, it has been investigated whether computer networking can benefit from these promising technologies as well.
Mowei Wang, Yong Cui, Xin Wang, Shihan Xiao, and Junchen Jiang:
Machine Learning for Networking: Workflow, Advances and Opportunities.
IEEE Network, Volume: 32, Issue: 2, March-April 2018.
Raouf Boutaba, Mohammad A. Salahuddin, Noura Limam, Sara Ayoubi, Nashid Shahriar, Felipe Estrada-Solano, and Oscar M. Caicedo:
A Comprehensive Survey on Machine Learning for Networking: Evolution, Applications and Research Opportunities.
Journal of Internet Services and Applications, 2018.
In this seminar participants will give talks about the most relevant machine learning methods and how they can be applied for computer networking problems. All talks will be based on state-of-the-art research papers and should be in English language. A written report can be either in German or English language.
- Introduction to Seminar PDF
- Introduction to Machine Learning Techniques PDF
- Traffic Classification PDF
- Traffic Prediction PDF
- Traffic Routing PDF
- Congestion Control PDF
- Resource Management / Scheduling PDF
- QoS and QoE Management PDF
- Security / Intrusion Detection PDF
- Vehicular Networks Overview and Congestion Control as an Example PDF
- Resource Management in Vehicular Networks PDF
For any questions please send a mail to Jörg Deutschmann