Machine Learning for Cities

(3 Credits)

The objective of this course is to familiarize students with advanced machine learning techniques and demonstrate how they can be applied to urban data. The course focuses on practice-oriented concepts and techniques, which are illustrated through applications to urban problems and datasets. For this reason, it includes a significant programming component, with Python as the primary language. Topics cover a variety of supervised and unsupervised learning methods, such as decision trees, support vector machines, clustering algorithms, text mining, and ensemble learning. Other key topics include an introduction to causal modeling, Gaussian processes, and anomaly detection. The course also explores strategies for effective machine earning and discusses the opportunities and limitations it brings.