Machine Learning for Civil Engineers¶
The accessibility of big datasets and computational advancements are transforming civil engineering research and practice. The availability of high resolution data coupled with ML models allows us to model highly nonlinear phenomena and processes that are ubiquitous in civil engineering. In this course we survey a range of ML algorithms to develop models for civil engineering applications. We shall look at both classical ML models as well as newer methods such as boosting, bagging, random forests and deep learners with a particular emphasis on spatio-temporal data.
Instructor Notes¶
This JupyterBook are my instructor notes that can serve as sort-of a textbook. There is a formal textbook listed below that was used by the prior instructor, it seems to me that the authors invented a calculus to explain machine learning in a mathematical “proof” context which is certainly nice, but most of us need to implement tools. I have chosen to suppliment with actual examples to replicate actual workflows one might encounter.