Machine Learning and Data Analytics for Civil Engineering Systems. 3 credits, 3 contact hours

Catalog description

Prerequisites: CS 101 , MATH 211 , and MATH 279 , or approval of instructor. This course provides students with hands-on and fundamental knowledge of machine learning, data science, and data mining methodologies for scraping, manipulating, transforming, cleaning, visualizing, summarizing, and modeling large-scale data in civil engineering and infrastructure systems using analytical tools. This course includes data management and analysis, data wrangling and exploration, unsupervised learning, pattern recognition, supervised learning for classification and regression purposes, data preprocessing, and model training and evaluation. Students will explore the capabilities of these concepts in addressing the challenging and interesting problems in the field of civil engineering, and they will develop skills to apply these techniques to solve multiple real-world civil engineering problems. Python programming language will be introduced and used throughout this course to illustrate practical examples and to show students how to apply the learned techniques in the civil engineering practice.