CSC461 Introduction to Machine Learning

[3–0, 3 cr.]

This course provides an overview of theoretical and application aspects of machine learning. Topics include supervised and unsupervised learning including generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines, clustering, dimensionality reduction, and kernel methods. The course also covers learning theory, reinforcement learning, adaptive control. An applied approach will be used, where students get hands-on exposure to ML techniques through the use of state-of-the-art machine learning software frameworks.

Prerequisite: CSC310 Algorithms and Data Structures