CIVE 7150 — Data-Driven Decision Support for Civil and Environmental Engineering

4 semester hoursGraduateLecturetypical days: T/FBostonTraditional

Presents supervised and unsupervised methods for dealing with large data sets and their application to support decision making in various civil and environmental engineering areas. Focuses on predictive models and methods for knowledge mining. Discusses applications from the transportation, urban mobility, and infrastructure maintenance domains. Topics include classification: linear regression, logistic regression, K-NN, and other classifiers; dimensionality reduction; clustering: K-means, hierarchical clustering, Gaussian mixture models, density-based clustering; model validation; and text mining. Demonstrates the applicability and underlying principles of the various methods through case studies with extensive data sets. Applications include classification of pavement distress images; mobility patterns; real-time transportation demand prediction; and text mining from reports. Background in probability and statistics and familiarity with Python/R recommended.

Offering history

TermSectionsEnrolledCapacityFullOpen seats/section
Spring 2024142020%16.0

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Professors

Spring

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Links

Official catalog (CIVE course descriptions) · All CIVE courses · Plan it at numap.app