Machine Learning - CSC 334

This course provides an overview of theoretical and applied machine learning. Topics include supervised and unsupervised learning, including parametric/non-parametric learning, support vector machines, random forests, clustering, dimensionality reduction, and kernel methods. The course also covers problem definition, basic exploratory data packages. In particular students will learn to use fluid dynamics engines designed for simulation and rendering of realistic fire, smoke, explosion and other gaseous phenomena. Prerequisite: CSC 277.