Machine Learning

Catalog description

An introduction to the theory and practice of machine learning with an emphasis on advanced mathematical foundations. Topics include supervised and unsupervised learning, model design and evaluation, regression, classification, clustering, and dimensionality reduction. Students will study core principles such as generalization, bias–variance tradeoff, and maximum likelihood estimation, and implement algorithms including linear and logistic regression, decision trees, random forests, k-nearest neighbors, principal component analysis (PCA), and neural networks.