Rutgers University 3 credits 544
This course covers the most fundamental aspects of machine learning. Bayes decision rule, regularization, overfitting, cross validation, AIC and BIC, bias-variance tradeoff, Bayesian point of view, maximum likelihood and maximum a posteriori, cross entropy. Methods such as k Nearest Neighbor, Naïve-Bayes, Classification and Regression Trees, random forest, support vector machines, linear and logistic regression, k-means and hierarchical clustering, Page rank, principal component analysis, and the expectation-maximization algorithms are covered. Software implementation and testing of algorithms and extensive use of machine learning libraries is utilized. A group semester project is required.
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