Civil & Environmental Engrg 697M - ST- DataMining&MachLrng/Engin

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. Hands-on programming in R throughout the course will enable students to perform analyses and learning on real-world datasets. Through this course, students will understand the potential of machine learning in civil, environmental and industrial engineering, as well as learn to create and train models from data to solve challenging problems. Prerequisites: basic knowledge of probability, statistics, linear algebra and calculus. Some programming experience in any language is helpful, but students should be ready to get up to speed with any necessary technical skills.