COSC 722 - Machine Learning Applications
3 Credits
The course is to study how to build computer systems that learn from experience. It is a subfield of Artificial Intelligence and intersects with statistics, cognitive science, information theory, and probability theory, among others. The course will explain how to build systems that learn and adapt using examples from real-world applications.
The class has a review session on probability and information theory will precede those chapters in need of background knowledge. Main topics include linear discriminants, neural networks, decision trees, support vector machines, unsupervised learning, reinforcement learning, and their applications.
Prerequisite(s) COSC 672
Offered (FALL or SPRING)
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