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May 07, 2021
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CSC 461 - Intro. to Machine Learning
This course provides an overview of theoretical and application aspects of machine learning. Topics include supervised and unsupervised learning including generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines, clustering, dimensionality reduction, and kernel methods. The course also covers learning theory, reinforcement learning, adaptive control. An applied approach will be used, where students get hands-on exposure to ML techniques through the use of state-of-the-art machine learning software frameworks.
3.000 Credit hours
3.000 Lecture hours

Levels: Undergraduate
Schedule Types: Lecture

Computer Science & Mathematics Department

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