Course Modules:
Module 1: Introduction to Smart Cities and AI
What makes a city “smart”?
Key AI technologies in urban systems
Stakeholders and smart governance models
Module 2: AI in Urban Mobility and Traffic Management
Real-time traffic prediction using machine learning
AI in public transport scheduling and routing
Autonomous vehicle integration and pedestrian safety
Module 3: Smart Energy and Utilities
AI for energy consumption forecasting
Smart grids and load balancing algorithms
Optimizing water usage and waste management
Module 4: Urban Planning and Infrastructure Analytics
Satellite imagery and AI for urban development
Predictive maintenance for infrastructure
Digital twins for city modeling
Module 5: Public Safety and Surveillance
AI-powered CCTV and facial recognition
Crime prediction models and anomaly detection
Emergency response optimization
Module 6: Environmental Monitoring and Sustainability
Air and water quality detection using sensors and AI
Waste sorting and recycling automation
AI for climate impact forecasting in cities
Module 7: Citizen Services and Personalized Engagement
Chatbots and AI for e-government
Sentiment analysis on citizen feedback
AI-enhanced accessibility for public services
Module 8: Privacy, Ethics, and Urban Data Governance
Data collection and consent in public AI systems
Avoiding surveillance bias and digital exclusion
Regulatory frameworks and compliance
Module 9: Tools and Technologies for Smart City AI
Platforms: TensorFlow, OpenCV, QGIS, Google Earth Engine
Cloud and edge deployment for smart infrastructure
Using public datasets (OpenStreetMap, NYC, EU Smart City)
Module 10: Final Capstone Project
Propose and prototype a smart city AI application such as:
Intelligent traffic light controller
Air quality prediction and alert system
AI chatbot for municipal services
Deliver project report, dataset, and functional demo
🧠Master Study NLP Fundamentals: The Foundation of Language Understanding in AI
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