PROEA 821 - Introduction to Machine Learning

This course, offered in collaboration with the University of Utah School of Computing, covers techniques for developing computer programs that can acquire new knowledge automatically or adapt their behavior over time. Topics include algorithms for supervised learning, decision trees, online learning, linear classifiers, empirical risk minimization, computational learning theory, ensemble methods, Bayesian methods, clustering. Class learning objectives: 1. Gain a broad theoretical and practical understanding of machine learning paradigms and algorithms, 2. Develop the ability to implement learning algorithms, 3. Identify where machine learning can be applied and make the most appropriate decisions (about algorithms, models, supervision, etc). Class requirements include 4 to 5 programming assignments, 4 to 5 online homework assignments and a project that will last the duration of the class.