This course provides an introduction to data-driven modeling and machine learning methods for engineering applications. It covers topics such as supervised learning, regression, classification, data driven dimensionality reduction techniques, and model validation, with emphasis on their connection to mathematical modeling and dynamical systems. The course combines theoretical foundations with practical implementation and highlights how data-driven approaches can enhance the modeling, analysis, and prediction of engineering systems. By the end of the course, students will be able to develop and implement machine learning models, analyze data-driven representations of engineering systems, and evaluate model performance and limitations.
Questions about the course?
Prof Gustavo Luiz Olichevis Halila – [email protected]
