DSC 255RMachine Learning Fundamentals
Supervised and unsupervised learning algorithms, and the theory behind those algorithms. Using case studies, covered topics include classification, regression, and conditional probability estimation; generative and discriminative models; linear models and extensions to non-linearity using kernel methods; ensemble methods: boosting, bagging, random forests; representation learning: clustering, dimensionality reduction, auto-encoders, deep neural networks. This is a distance education course.
Prerequisites
From the catalog: “DSC 215R. Restricted to major codes DS78 and DS79. All other students with graduate standing may be considered as space permits.”