Instructor: Mohamad Alipour

Location: 2015 Civil and Environmental Engineering Bldg.

Times: Tue-Thu, 2:00PM - 3:20PM

Office Hours: TBA at Newmark 2114

Course Flyer: Link

Course Description: Restoring and improving the built and natural environments in the face of increasing demand, aggravating natural hazards, and climate change calls for civil and environmental engineers equipped with emerging data science skills. The proliferation of sensing, collection of rich data, and powerful deep learning techniques are increasingly transforming both the challenges faced and the solutions available to civil engineers. 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, generative adversarial learning, and physics-informed neural networks. 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 when, 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.

Recommended Prerequisite: CEE492 Data Science for CEE or equivalent.