[Cee-grad-students] Large Language Models (LLMs) CE Elective Offered Fall 2025: CE 563
Hello CE Seniors and Graduate Students, CE 563: Transportation and Logistics Optimization and Modeling is a 4-credit course offered this fall that will explore the fundamentals of mathematical modeling and optimization using Python, with a forward-looking focus on integrating Large Language Models (LLMs). You'll learn to translate real-world problems into mathematical formulations and apply a variety of optimization techniques—including linear, nonlinear, and integer programming—to find effective solutions, not only in transportation but in any field of study. *Undergraduate CE students:* the prerequisite to CE 563 is CE 316: Applied Probability and Statistics for Civil & Environmental Engineering. If you have completed CE 316 and would like to register, respond to this email. CE 563 will count as a CE elective. *Graduate CE students *should confirm with their faculty advisor before registering. More details about this course: *Integrating LLMs for Enhanced Problem Solving*Beyond traditional modeling, this course emphasizes the strategic use of LLMs to support code generation, interpretation, and validation. While LLMs are powerful tools, it's crucial to understand their limitations. You'll learn to integrate human expertise for critical validation steps and sensitivity analysis, ensuring the reliability of your models. A central theme is learning to solve the right problem, not just producing elegant solutions. You'll confront the real-world challenge of properly framing questions and understanding the modeling outputs. This develops critical thinking and domain awareness, ensuring that AI tools generate meaningful outcomes rather than optimizing irrelevant or poorly scoped problems. *Hands-on Learning and Prerequisites*Throughout the course, you'll gain hands-on experience using Python libraries for mathematical modeling while also learning effective prompt engineering for LLMs. NOTE: Laptops are required, as is a background in calculus and linear algebra. While familiarity with Python (or any programming language) is recommended, it's not a strict prerequisite given that LLMs will be used to support coding. CEE Department -- Department of Civil and Environmental Engineering Portland State University Phone 503.725.4282 ceedept@pdx.edu www.pdx.edu/cee
Hello CE Seniors and Graduate Students, CE 563: Transportation and Logistics Optimization and Modeling is a 4-credit course offered this fall that will explore the fundamentals of mathematical modeling and optimization using Python, with a forward-looking focus on integrating Large Language Models (LLMs). You'll learn to translate real-world problems into mathematical formulations and apply a variety of optimization techniques—including linear, nonlinear, and integer programming—to find effective solutions, not only in transportation but in any field of study. *Undergraduate CE students:* the prerequisite to CE 563 is CE 316: Applied Probability and Statistics for Civil & Environmental Engineering. If you have completed CE 316 and would like to register, respond to this email. CE 563 will count as a CE elective. *Graduate CE students *should confirm with their faculty advisor before registering. More details about this course: *Integrating LLMs for Enhanced Problem Solving*Beyond traditional modeling, this course emphasizes the strategic use of LLMs to support code generation, interpretation, and validation. While LLMs are powerful tools, it's crucial to understand their limitations. You'll learn to integrate human expertise for critical validation steps and sensitivity analysis, ensuring the reliability of your models. A central theme is learning to solve the right problem, not just producing elegant solutions. You'll confront the real-world challenge of properly framing questions and understanding the modeling outputs. This develops critical thinking and domain awareness, ensuring that AI tools generate meaningful outcomes rather than optimizing irrelevant or poorly scoped problems. *Hands-on Learning and Prerequisites*Throughout the course, you'll gain hands-on experience using Python libraries for mathematical modeling while also learning effective prompt engineering for LLMs. NOTE: Laptops are required, as is a background in calculus and linear algebra. While familiarity with Python (or any programming language) is recommended, it's not a strict prerequisite given that LLMs will be used to support coding. CEE Department -- Department of Civil and Environmental Engineering Portland State University Phone 503.725.4282 ceedept@pdx.edu www.pdx.edu/cee
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CEE Department