Machine Learning

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 analysis, and reinforcement learning. An applied approach will be used, where students get hands-on exposure to machine learning techniques using state-of-the-art frameworks, such as Scikit-Learn, Keras, NumPy, Pandas, and Matplotlib. Prerequisites: CSC 210, CSC 213, or CSC 215. 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.