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CS 6140 - Machine Learning |
Provides a broad look at a variety of techniques used in machine learning and data mining, and also examines issues associated with their use. Topics include algorithms for supervised learning including decision tree induction, artificial neural networks, instance-based learning, probabilistic methods, and support vector machines; unsupervised learning; and reinforcement learning. Also covers computational learning theory and other methods for analyzing and measuring the performanceof learning algorithms. Course work includes a programming term project.
4.000 Credit hours 4.000 Lecture hours Levels: Graduate, Undergraduate Schedule Types: Lecture Computer Science Department Course Attributes: GSCS Computer & Info Science Restrictions: Must be enrolled in one of the following Programs: PhD Computer Science MSCS Computer Science MSCS Computer Science - Align MS Artificial Intelligence MS Robotics Must be enrolled in one of the following Levels: Graduate Must be enrolled in one of the following Classifications: Graduate Prerequisites: Graduate level CS 5800 Minimum Grade of C- or Graduate level CS 7800 Minimum Grade of C- |
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