DSSA5201

DSSA

5201

An introduction to algorithms and techniques for predictive modeling and pattern recognition. Students will have the opportunity to use established libraries that implement supervised learning methods (e.g., k-nearest neighbors, linear and logistic regression, decision trees, random forests, support vector machines) and unsupervised learning methods (e.g., k-means clustering, principal component analysis) to authentic datasets; to train a model and evaluate and improve its performance; and to begin developing an intuition for matching a method or algorithm to a dataset for optimal performance. Interested students in programs other than DSSA should contact the instructor for permission to register.