Course: COMP 442. Introduction to Machine Learning (3)
Prerequisites: COMP 182/L and MATH 444; or COMP 380/L and MATH 340; or COMP 380/L and MATH 341. 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. Available for graduate credit. Graduate students will be required to complete advanced projects.