TR-GY 7083 Analytics and Learning Methods for Smart Cities

3 Credits

Basics of analytics and learning methods, with applications in smart cities. Introduction of algorithms in their very basic forms. Implementation of common machine learning algorithms in coding languages. Smart city applications of machine learning algorithms. Topics include probability review, inference, linear regression, classification, neural networks, and introduction to reinforcement learning. Applications include autonomous vehicles, traffic control, public transit, ridesharing, urban emergency response, smart grid, and smart buildings. Intended for students interested in smart city applications.

Prerequisite(s): Knowledge of Statistics or Data Analysis or equivalent