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CIVE 650 Spatiotemporal Data Mining (4 credits)

Offered by: Civil Engineering (Faculty of Engineering)

Administered by: Graduate Studies


Civil Engineering : Introduction to spatiotemporal sensing data and general research questions (imputation, forecasting, kriging, classification, regression, anomaly detection). Overview of traditional models and techniques in modeling temporal processes, spatial processes, and spatiotemporal processes. Introduction of state-of-the-art methods for large-scale data sets, including low-rank tensor learning, spectral methods, Gaussian process regression, graph signal processing, and deep neural networks.

Terms: Fall 2022

Instructors: Sun, Lijun (Fall)

  • Prerequisite: Permission of the instructor.

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