Machine Learning

Catalog description

Machine Learning comprises the broad discipline of developing algorithms and statistical models to predict, classify, or cluster data and iteratively improve over time. Machine Learning focuses on building, training, running, and testing supervised and unsupervised models and quantifying the accuracy and precision of those models to determine which may best be used in a particular business situation. Supervised methods discussed include k-nearest neighbors, decision trees, and support vector machines. Unsupervised models discussed include k- means clustering, hierarchical clustering, and t-distributed stochastic neighbor embedding (t-SNE). Ensemble methods are also presented. The following courses are prerequisites: Analytics Programming and Statistical Data Mining.