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Nov 25, 2024
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CSC 272 - Machine Learning
Semester Hours: 3
Periodically
The course introduces the mathematical, algorithmic and practical aspects of machine learning. Students will learn how to design applications that learn from data and past experience. Applications include classification, clustering, prediction, decision making. Among topics covered in the class are: regression, neural networks, decision trees, support vector machines, model and feature selection, ensemble methods, boosting, clustering, graphical models.
Prerequisite(s)/Course Notes:
CSC 204 or permission by instructor. May not be taken on a Pass/Fail basis.
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