CEE 598

Fall 2025 Part of Term 1

Part of Term 1
Aug 25-Dec 10

Special Topics

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Credit: 1 TO 4 hours.

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.

May be repeated in the same or separate terms if topics vary.

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CEE 598 class schedule data for fall 2025
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
39918	Lecture-Discussion	DL	2:00PM -3:20PM	TR	2015 Civil & Envir Eng Bldg	Alipour, M Zhao, Y	Part of Term: 1 Date Range: 08/25/25-12/10/25 Credit: 4 hours Section Title: Deep Learning for CEE Section Info: 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
55907	Online	DLC	ARRANGED	n.a.	n.a.	Alipour, M Zhao, Y	Part of Term: 1 Date Range: 08/25/25-12/10/25 Special Approval: Departmental Approval Required Credit: 4 hours Section Title: Deep Learning for CEE Section Info: Deep Learning for CEE 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 Restriction(s): Restricted to Graduate - Urbana-Champaign.
47399	Online	DLO	ARRANGED	n.a.	n.a.	Alipour, M Zhao, Y	Part of Term: 1 Date Range: 08/25/25-12/10/25 Credit: 4 hours Section Title: Deep Learning for CEE Section Info: 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 Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to MS: Civil Engr - Online - UIUC, MS:Industrial Engr Online-UIUC, MS:Mechanical Engineerng -UIUC, MS:Env Engr CivilEngr ONL-UIUC, NDEG:Engineering GR ONL - UIUC, MS: Aerospace Engr-Online-UIUC, MENG:Mech Engineering Onl-UIUC, MENG:Engr:Energy Sys Onl-UIUC, MENG:Engr:AeroSys Online- UIUC, or MENG:ENGR:Digital Ag ONL- UIUC.
31554	Lecture-Discussion	UTM	3:30PM -4:50PM	TR	3310 Newmark Civil Engineering Bldg	Kontou, E	Part of Term: 1 Date Range: 08/25/25-12/10/25 Credit: 4 hours Section Title: Urban Transportation Models Restriction(s): Restricted to Graduate - Urbana-Champaign.
60530	Online	UTO	ARRANGED	n.a.	n.a.	Kontou, E	Part of Term: 1 Date Range: 08/25/25-12/10/25 Credit: 4 hours Section Title: Urban Transportation Models Restriction(s): Restricted to MS: Civil Engr - Online - UIUC, MS:Industrial Engr Online-UIUC, MS:Mechanical Engineerng -UIUC, MS:Env Engr CivilEngr ONL-UIUC, MS: Aerospace Engr-Online-UIUC, NDEG:Graduate OR - UIUC, MENG:Mech Engineering Onl-UIUC, MENG:Elec & Comp Eng ONL -UIUC, MENG:Engr:Energy Sys Onl-UIUC, MENG:Bioeng:Gen Bioeng On-UIUC, MENG:Bioeng:Bioinstr Onl -UIUC, or MENG:Engr:AeroSys Online- UIUC.