Course Outline

Introduction to SLMs in Smart Cities

  • Defining Small Language Models
  • The role of AI in urban development
  • SLMs as a tool for smart city innovation

SLMs and Urban Data Analysis

  • Collecting and processing urban data
  • Using SLMs for data-driven urban planning
  • Enhancing public services with SLM insights

Implementing SLMs for Urban Management

  • Integration of SLMs in traffic and transportation management
  • SLMs for environmental monitoring and sustainability
  • Public engagement and participatory urban planning with SLMs

Evaluating the Effectiveness of SLMs in Urban Planning

  • Measuring the outcomes of SLM implementations
  • Learning analytics for smart city initiatives
  • Feedback mechanisms and continuous improvement

Challenges and Future Directions

  • Addressing privacy and ethical concerns
  • Scalability and maintenance of SLM systems
  • Future trends and advancements in smart city AI

Project Work: Developing a Smart City Solution

  • Conceptualizing a smart city project using SLMs
  • Hands-on development and testing
  • Presentation of projects and group critique

Summary and Next Steps

Requirements

  • Basic understanding of urban planning principles
  • Familiarity with AI and machine learning concepts
  • Interest in smart city technologies and their applications

Audience

  • Urban planners
  • City administrators
  • Smart city solution developers
 14 Hours

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