12-778 Special Topics: Sensors, Data, and Models in Engineering Systems
12 units, Carnegie Mellon University
AI and machine learning are rapidly becoming standard tools for civil and environmental engineering, but their performance depends on how data are measured, stored, and connected to the physical systems they represent. This course provides the end-to-end foundations needed to develop reliable data-driven solutions for engineering problems (from sensing and digitization to data management and modeling) serving as a bridge into graduate machine learning. The course begins with measurement physics and instrumentation, studying how physical processes are transduced into electrical signals, conditioned and filtered, and converted to digital information. Topics include common sensor modalities, interface circuits, DC/AC circuit analysis, amplification, filtering, frequency-domain analysis, and analog-to-digital conversion. The course then develops the "data layer" required for scalable deployments, including uncertainty and measurement error, compression and representation, metadata, and database design. Finally, students learn modeling and inference concepts that prepare them for ML across learning paradigms, including supervised learning foundations (regression, regularization, validation, and generalization) and unsupervised methods for structure discovery and monitoring (dimensionality reduction, clustering, and anomaly detection). A final project integrates these components to design a sensing-to-modeling pipeline for a civil or environmental engineering application, emphasizing domain knowledge and physical consistency throughout the designed solution.
Prerequisites: none listed
Offered: Fall 2026
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