This course provides a comprehensive introduction to machine learning specifically designed for civil and environmental engineering applications. It guides learners from basic concepts to advanced topics, focusing on how data-driven methods can be used to solve real engineering problems.

The course begins with an introduction to teaching methods, course structure, and how to access datasets and coding resources. It then explains the fundamentals of machine learning, including its purpose, applications, and role in modern engineering.

Learners are introduced to data and statistics, which form the foundation for all machine learning models. The course then explores common machine learning algorithms and how they are applied to engineering problems such as prediction, classification, and system modeling.

A key focus is placed on performance metrics and error evaluation, helping students understand how to measure model accuracy and reliability. The course also compares coding-based and no-code approaches, making it accessible to learners with different technical backgrounds.

Advanced topics include explainability and interpretability, causal inference, and causal discovery, which are essential for understanding how and why models make predictions.

By the end of this course, learners will be able to apply machine learning techniques to civil and environmental engineering problems, analyze data effectively, and build data-driven solutions.