CSCI 516 - Introduction to Machine Learning

(3) Prerequisite(s): Algorithms, Linear Algebra

Machine learning (ML) is the study of predictive models whose performance can be improved by incorporating additional data or experience. This course will give an overview of the theory and practice of machine learning, focusing primarily on deterministic ML methods for classifcation and regression. Topics include decision trees, linear and nonlinear regression, artifcial neural networks, support vector machines and kernel methods, ensemble methods, clustering methods, dimension reduction techniques, mixture models, and naive Bayes methods. We will also look at practical concerns such as performance evaluation, data preprocessing, and hyperparameter tuning. Cross-listed with CSCI-416