Course Detail
Course Components:
Enrollment Information
Enrollment Requirement:
Prerequisites: Minimum of 2 semesters of graduate level statistics and a foundational knowledge of matrix algebra and calculus. At least one semester of a programming course (C++, Python, etc) is recommended. Course taught in R.
Machine learning is concerned with algorithms that improve their performance as more data is accumulated. This course covers a range of topics in supervised and unsupervised learning, as well as relevant aspects in estimation and generalizability. Specific topics include dimension reduction, clustering, density estimation, point and variance estimation, variable selection and regularization, accuracy assessment, classification, and ensemble methods.