PRINCIPLES OF MACHINE LEARNING

Catalog description

Machine learning is a rapidly growing field that powers many of the services we use today. This is a graduate-level course that covers advanced machine-learning concepts in depth. The topics in this course include (but are not limited to) global/local optimization, gradient descent/ascent, regression, categorical cross entropy, classification, logistic regression, matrix factorization, feature engineering, feature selection, boosting, and regularization. Prerequisite: COMP5700 (3 credits)