CIVE 650 — Spatiotemporal Data Mining

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.

• Credits: 4
• Faculty: Graduate Studies
• Department: Civil Engineering
• Taught by: Lijun Sun
• Prerequisite: Permission of the instructor.

Sections offered

• Section 001 (Lec), Mon 11:35 am-2:25 pm — 16 seats open