Theory and applications of core concepts in data mining and machine learning from an engineering perspective. Key topics: fundamentals of data analysis, regression, unsupervised learning (clustering, dimensionality reduction, etc), classification (support vector machines, decision trees). Model assessment & inference, additive models and neural networks will also be covered, with a big data focus. Applications to various subdisciplines will be highlighted, especially in transportation, environmental, structural and industrial engineering.