Course: COMP 542. Machine Learning (3)
Prerequisites: COMP 380/L and MATH 340 or MATH 341; or graduate standing. A study of the concepts, principles, techniques, and applications of machine learning. Topics include concept-based learning, information-based learning (decision trees and ID3 algorithms), rule-based learning (association rules, learning ordered rules, learning unordered rules, and descriptive rule learning), distance-based learning (nearest neighbor algorithms), probability-based learning (Bayesian classifiers and networks), and error-based learning (perceptron, multivariable linear regression with gradient descent, nonlinear and multidimensional models, artificial neural networks, and support vector machines). Model ensembles learning and reinforcement learning are also discussed.