Establishing hands-on skills along with understanding of underlying mathematical concepts of current machine learning approaches including: ordinary least squares, quantile, logistic, and local regression; unsupervised methods including principal component analysis and clustering; tree-based models such as regression trees and random forests; kernel-based methods such as support vector and Gaussian process regression; Bayesian inference; as well as shallow and deep neural networks. Numerous examples and case studies applicable to thermal/building/renewable/district energy systems will be used. Undergraduate seniors will be allowed with instructor consent.
