Course Modules

Module 1: Artificial Intelligence Transformation in Telecommunications Networks

• Evolution of telecom networks from traditional systems to intelligent networks
• Role of AI in telecommunications digital transformation
• Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI concepts
• Telecom AI use cases across mobile, broadband, and digital services
• Industry challenges driving AI adoption:
  • Network complexity
  • Increasing data volumes
  • Operational cost pressures
  • Demand for ultra-reliable connectivity
• AI opportunities in 5G, 5G Advanced, and 6G ecosystems
• Overview of autonomous and self-healing networks
• Case Study & Practical Application: Assessing the AI maturity level of a telecom operator and developing an AI adoption roadmap for network transformation.

Module 2: Telecom Data Analytics for Intelligent Network Management

• Role of data analytics in modern telecom operations
• Sources of telecom network data:
  • Network performance data
  • Customer usage data
  • Equipment logs
  • IoT and sensor data
• Telecom Key Performance Indicators (KPIs) and analytics
• Real-time data processing and streaming analytics
• Data quality management and preparation for AI applications
• Cloud-based telecom analytics platforms
• Data governance and privacy considerations
• Case Study & Practical Application: Analysing telecom network datasets to identify performance bottlenecks and operational improvement opportunities.

Module 3: Machine Learning for Network Optimization and Performance Improvement

• Fundamentals of machine learning for telecom applications
• Supervised and unsupervised learning approaches
• Predictive modelling for network performance
• Traffic forecasting and capacity planning
• Network anomaly detection
• Intelligent resource allocation
• AI-assisted spectrum optimization
• Predictive quality-of-service management
• Case Study & Practical Application: Developing a machine learning model to predict network congestion and recommend optimization measures.

Module 4: AI-Driven Network Automation, Fault Management and AIOps

• Introduction to Artificial Intelligence for IT Operations (AIOps)
• AI-based network monitoring and event correlation
• Intelligent fault detection and root cause analysis
• Automated incident management
• Predictive maintenance strategies
• Self-Optimizing Networks (SON)
• Network orchestration and automation
• Integration of AI with Software Defined Networking (SDN) and Network Functions Virtualization (NFV)
• Case Study & Practical Application: Designing an AI-powered Network Operations Centre (NOC) workflow for automated monitoring, fault detection, and resolution.

Module 5: Future Intelligent Networks, AI Governance and Emerging Technologies

• Autonomous network architectures
• Digital twins for telecom network simulation
• Edge AI and distributed intelligence
• AI applications in 6G network evolution
• Responsible AI principles in telecommunications
• AI security risks and mitigation strategies
• Data privacy and regulatory compliance
• Future trends in AI-enabled telecommunications
• Case Study & Practical Application: Developing a future-ready AI strategy for a telecom operator integrating autonomous networks, cybersecurity, governance, and emerging technologies.