AI+ Telecommunications

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

1. 1

   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
2. 2

   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
3. 3

   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
4. 4

   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
5. 5

   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
6. 6

   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
7. 7

   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
8. 8

   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
9. 9

   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