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MATH 213 - Supervised Machine Learning |
This course is an overview of machine learning techniques that use labeled data to train an algorithm to make predictions about unlabeled data. It provides an introduction to both linear regression and to classification techniques including logistic regression, K-nearest neighbors, support vector machines, tree-based methods, and neural networks. Prerequisites: DSCI 230, MATH 208, and MATH 211. Three credit hours.
3.000 Credit hours 3.000 Lecture hours Levels: Second Degree, Undergraduate Schedule Types: Lecture Math & Computing Department |
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