Machine learning is transforming the way we approach problems across all fields of civil and environmental engineering. This course is designed for students across various concentrations to offer a broad perspective on how data science and machine learning can reshape the future of Civil and Environmental engineering. You will learn how machine learning techniques can be applied to analyze and improve a wide range of engineering systems-from smart buildings and geotechnical infrastructure to water resources and transportation networks. Whether working with big data, such as GPS tracking data for traffic flow, satellite images for environmental monitoring, and IoT sensor data in buildings, or small data, such as soil properties for construction site assessment and water distribution system restoration after disasters, machine learning can uncover patterns and insights to improve efficiency, reliability, and resilience of the components or systems. Students will learn how to select and build models for regression, classification, and clustering, and apply them to tackle real-world challenges across various engineering disciplines. Through case studies and practical exercises in Python and Jupyter Notebooks, you will also gain valuable skills in exploratory data analysis, machine learning model construction and validation, and result visualization. Credit not granted for both AREN 4318 and CE 4318. Prerequisite: Grade of A or better in AREN 3301 or consent of instructor and admission to the AREN Professional Program.