MATH 5790 MACHINE LEARNING WITH LINEAR MODELS
This course introduces principles, algorithms, and applications of machine learning using linear models from the point of view of modeling and prediction. It includes the formulation of learning problems and concepts of representation, over-fitting, and generalization. The course provides a rigorous introduction to linear classification and regression for supervised learning, covering concepts such as perceptron, features, and margin maximization. This course requires the use of computers.
Prerequisite
MATH 2995 and MATH 2416 or instructor’s approval