Urban Computing and Artificial Intelligence

Catalog description

The course aims to equip students with the technical and analytical capacity to use state-of-the-art AI techniques for urban data science. It also seeks to foster thought leadership by encouraging critical thinking about the societal implications of urban computing and AI applications. This course begins with a recap of deep learning methods for spatial-temporal data and its practical applications in urban management. The course then delves into generative AI, providing an overview of models such as GPT and Large Language Models (LLMs), with a focus on practical applications for urban governance and modeling. The course concludes by introducing limitations and open questions including fairness and bias issues, techniques for causal inference and decision-making, and modeling urban complexity. This comprehensive course aims to not only provide technical skills but also to nurture a deep understanding of the broader implications and potentials of urban computing and AI in shaping future cities.