PDAT 615G - Machine Learning

This course introduces the theory and practice of machine learning. Statistical learning techniques such as regression, regularization, and principal component analysis are covered. Programming in a popular machine learning language such as R is reviewed. Approaches such as neural networks, support vector machines, unsupervised learning, and reinforcement learning are covered.

Credit(s): 3
Prerequisite(s): PDAT 610G - Introduction to Data Science OR [(STAT 220 - Fundamentals of Data Science or DATA 222 - Data Science) and (STAT 250 - Statistical Computing or DATA 322 - Intermediate Data Science)], or concurrent enrollment.
Registration Restriction(s): Graduate Students Only.

Disciplinary Perspective(s):
None
Interconnecting Perspective(s):
None
University Graduation Requirement(s):
None