MACHINE LEARNING FUNDAMENTALS

Catalog description

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.