MATH 470 Machine Learning

4 semester hours

Linear regression, logistic/softmax regression, support vector machine, k-nearest neighbors, tree-based methods, linear separability, overfitting/underfitting, regularizers, gradient descent method.  Possible additional topics include: kernel methods, k-means clustering, principal component analysis, dimensionality reduction, semi-supervised learning, boosting, random forest, and sampling methods. 

Prerequisites: MATH 234 and MATH 251 and CMSI 1010 or consent of instructor. 

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