Introduction to Machine Learning

3 Credits

Techniques of applied machine learning rather than deep theory behind the algorithms and methods. Programming solutions for machine learning problems using a high-level programming language and associated machine learning libraries. Regression, clustering, principal component analysis, Bayesian methods, decision trees, random forests, support vector machines, and neural networks. This course is a version of ISE 364 for graduate students, with advanced assignments. A student cannot receive credit for both ISE 364 and ISE 464 .