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COSC-688 Machine Learning
Fall only
This course surveys the major research areas of machine learning, concentrating on inductive learning. The course will also compare and contrast machine learning with related endeavors, such as statistical learning, pattern classification, data mining, and information retrieval. Topics will include rule induction, decision trees, Bayesian methods, density estimation, linear classifiers, neural networks, instance-based approaches, genetic algorithms, evaluation, and applications. In addition to programming projects and homework, students will complete a semester project.
Credits: 3
Prerequisites: Graduate students only
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