06-19, 10:15–11:45 (America/New_York), Potomac Ballroom
This course instructs participants on how to conduct Wi-Fi Health Checks using machine learning (ML). It explores AI and ML technologies tailored for enhancing Wi-Fi network health, addressing issues like interference and congestion. Integrating AI into Wi-Fi monitoring sustains robust connectivity crucial for remote work, online learning, and digital entertainment. Participants gain practical experience in ML techniques for network analysis and optimization. Prerequisites include basic Python knowledge and Internet connectivity. Upon completion, attendees will possess a comprehensive understanding of ML's application in improving Wi-Fi network health.
Incorporating AI into Wi-Fi health monitoring is vital for sustaining robust connectivity, essential for remote work, online learning, telehealth services, and digital entertainment in homes. Improved Wi-Fi network performance and reliability lead to enhanced productivity and satisfaction across various societal sectors and family environments. By minimizing network disruptions, ML-driven Wi-Fi Health Checks contribute to a seamless digital experience, supporting the ever-increasing reliance on Internet connectivity in both public and private spheres.
Outcomes:
Participants will gain practical experience with ML models and techniques for analyzing and optimizing Wi-Fi networks. They will:
- Understand case studies demonstrating the successful application of ML in boosting Wi-Fi network efficiency and reliability.
- Learn to identify relevant Wi-Fi network features to detect interference and congestion.
- Analyze data and metrics to make strategic decisions for network improvement.
- Apply ML algorithms to detect a decrease in the quality of experience for network users.
After completing the course, attendees will possess a good understanding of the application of machine learning in improving Wi-Fi network health and performance across different environments. They will receive a certificate of completion.
Prerequisites:
- Basic knowledge of Python and Python libraries management (pip)
- Internet connection during the course
- A Google Colab account
Murat Bilgic has a Ph.D. in Electrical Engineering and is the VP of Technology Realization at B-Yond, with past technology leadership roles at TMO, Nortel, EXFO and others. He has 30 years of experience in designing wireless data networks specializing in GSM, UMTS, LTE, 802.11, UMA, IP, virtualization and software defined networking, design, deployment, testing, standardization, network planning, capacity analysis, and intellectual property generation.