This course introduces students to machine learning. If provides students with a broad overview of machine learning topics for both supervised and unsupervised methods. The topics typically include classification, decision trees, association rule-based classification, support vector machines, regression (linear, logistic and Bayesian), clustering, k-Nearest Neighbor, principal component analysis (PCA), Feature Selection, Linear Discriminant Analysis (LDA) and Factor Analysis. Additional topics can include ensemble methods such as stacking, bagging and boosting. (Prerequisite: DATS311)

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