The Grainger College of Engineering
Civil & Environmental Engineering

CEE 598 DLO

CEE 598 DLO - Deep Sensing for CEE

Fall 2024

TitleRubricSectionCRNTypeHoursTimesDaysLocationInstructor
Deep Learning for CEECEE598DLO47399ONL4 -    Mohamad Alipour

Official Description

Subject offerings of new and developing areas of knowledge in civil and environmental engineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: May be repeated in the same or separate terms if topics vary.

Section Description

This course focuses on deep learning within all areas of civil and environmental engineering. In addition to examining the basics of deep learning, students will investigate practical applications in sensor data processing, information extraction, remote sensing, surrogate modeling, and predictive analytics. Topics of interest include deep convolutional networks, recurrent neural networks, and generative adversarial learning. Students will learn to identify, understand, and compare different deep learning techniques and formulate civil engineering problems using appropriate techniques. The focus will be on understanding why and how deep learning methods may improve civil engineering problem-solving and determining the conditions when deep learning may not be a helpful approach. Ultimately, the concepts will be leveraged to formulate and solve data-intensive real-world CEE problems using the techniques discussed. Prerequisite: CEE 492, or equivalent

Subject Area

  • Civil and Environmental Engineering

Course Description

Deep Learning for CEE Sensing, Simulation, & Prediction. This course focuses on deep learning within the civil and environmental engineering domain. In addition to examining the basics of deep learning, students will investigate practical applications in remote sensing, sensor data processing, information extraction, surrogate modeling, and predictive analytics. Topics of interest include deep convolutional networks, recurrent neural networks, and generative adversarial learning. Students will learn to identify, understand, and compare different deep learning techniques and formulate civil engineering problems using appropriate techniques. The focus will be on understanding why and how deep learning methods may improve civil engineering problem-solving and determining the conditions when deep learning may not be a helpful approach. Ultimately, the concepts will be leveraged to formulate and solve data-intensive real-world CEE problems using the techniques discussed.

Credit Hours

4 hours

Prerequisites

Undergraduate degree.

Last updated

11/16/2023