Michigan State University 3 credits Mathematics
Regression, clustering, dimension reduction, density estimation, anomaly detection, classification, and related methods (e.g., k-nearest neighbors, support vector machines, neural networks, decision trees, random forests), autoencoders, generative adversarial networks, and existing machine learning tools, training methods, and software. Prerequisite: (MTH 309 or MTH 314 or MTH 317H) and (CSE 231 or CMSE 201).
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