The availability of big data is transforming Civil Engineering profession. Data-driven tools and algorithms have the promise to model nonlinear civil engineering phenomena and extract information that is not possible through traditional modeling methods. Machine learning is a fast-growing field and its use in civil engineering will likely become routine in the next few years. The primary objective of this course is to provide an introduction and exposure to ML applications in Civil Engineering.

The course is mathematically oriented and will require developing scripts (short computer programs) using R statistical and programming environment and Python and associated libraries. No prior experience with Python is necessary, but familiarity with programming concepts covered in CE 1330 – Computational Thinking with Data Science and in CE 5315 - Probabilistic Methods for Engineers is expected. Students are strongly encouraged to take CE 5310 Numerical Methods in Engineering prior to this course.

Upon completion of this course, students should be able to:

Document work using JupyterLab Notebooks running a python kernel

Download large datasets using scripts to automate the retreival process

Perform Exploratory Data Analysis and plot meaningful relationships

Structure unconstrained and constrained optimization problems

Build prediction engines using various regression techniques

Build classification engines using various classifier techniques

Select appropriate algorithms for a dataset

CE 5319 Machine Learning for Civil Engineers