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Machine learning uses interdisciplinary techniques such as statistics, linear algebra, and optimization to create automated systems that can sift through large volumes of data at high speeds to make predictions or decisions. This class will cover topics in linear regression (multivariate, subset selection, RIDGE & LASSO, and model selection), basic classification methods (kNN, Naïve Bayes, logistic regression, LDA, and SVM), and the concept of unsupervised learning (k-means cluster and PCA). Prereq: IME 460.