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This course provides students a basic understanding of the subject of machine learning. Topics include: supervised learning (regression, logistics regression, support vector machines, tree classification methods, and ensemble methods, such a bagging, boosting, and random forests), unsupervised learning (k-means, hierarchal, and clustering), and techniques for evaluating machine learning algorithms.
