Recommended Prerequisites:
- Telecommunications Knowledge: Basic understanding of telecommunications concepts, including networks, 5G, and IoT.
- Programming Skills: Familiarity with programming, preferably in Python.
- Data Analysis: Basic knowledge of data analysis techniques is beneficial.
- AI Familiarity: Prior experience with AI is helpful but not required for enrollment in this course.
Course Outline:
Lesson 1: Introduction to AI in Telecommunications
- 1.1 AI Fundamentals in Telecommunications
- 1.2 AI Technologies for Telecom
- 1.3 Emerging Trends in AI for Telecommunications
- 1.4 Case Study
- 1.5 Hands-on
Lesson 2: Data Engineering for Telecom AI
- 2.1 Foundation of Telecom Data Engineering
- 2.2 Designing and Managing the Telecom Data Pipeline
- 2.3 Data Engineering tools and Technology
- 2.4 Case Study: SK Telecom’s Big Data Analytics with Metatron Discovery
- 2.5 Hands on Exercise
Lesson 3: AI for 5G Networks
- 3.1 Introduction to 5G
- 3.2 AI Applications in 5G
- 3.3 Enhancing Network Management with AI
- 3.4 Case Study
- 3.5 Hands-on
Lesson 4: AI in Network Optimization
- 4.1 Predictive Network Management
- 4.2 Performance Enhancement Techniques
- 4.3 Traffic Management Strategies
- 4.4 Case Study
- 4.5 Hands-on
Lesson 5: AI in Network Security
- 5.1 Security Threats in Telecom
- 5.2 AI Security Solutions
- 5.3 Advanced Security Frameworks
- 5.4 Case Study
- 5.5 Hands-on
Lesson 6: Enhancing Customer Experience with AI
- 6.1 Personalized Customer Service
- 6.2 Service Quality Improvement
- 6.3 Enhancing Customer Engagement
- 6.4 Case Study
- 6.5 Hands-on
Lesson 7: IoT Integration with Telecommunications
- 7.1 IoT Fundamentals
- 7.2 Managing IoT Security Challenges
- 7.3 Enhancing Operational Efficiency with IoT
- 7.4 Case Study
- 7.5 Hands-on
Lesson 8: AI-Integrated Network Operations Centers (NOC)
- 8.1 Transitioning to AI-driven NOCs
- 8.2 Automating escalations and root cause analyses
- 8.3 Closed-loop automation with AI and SDN integration
- 8.4 Designing AI-ready network architectures
- 8.5 Change management strategies for AI rollouts in operations
- 8.6 Case Study: Implementation of AI assistants in NOCs
Lesson 9: Ethical Considerations in Artificial Intelligence
- 9.1 Ethical Implications of Using Artificial Intelligence
- 9.2 Responsible Deployment Practices
- 9.3 Emerging Trends and Challenges
- 9.4 Case Study
- 9.5 Hands-on
