Artificial Intelligence Concentration

This course introduces students to data mining and its various applications. Students will gain proficiency in supervised data mining techniques for building prediction models such as decision trees, random decision forests, bootstrapping, training and testing using multi-fold cross-validation and using entropy measures for weighting features. Students will also use unsupervised data mining techniques such as clustering and association analysis. Both methods are used for discovering patterns and associations in data.

This is an introductory course in linear algebra. Topics include systems of linear equations, matrix algebra, determinants, eigenvalues & eigenvectors, and an introduction into the theory of vector spaces.

Over the past two decades several applied sub-disciplines of Artificial Intelligence (AI) have enabled new computing capabilities and replaced more traditional algorithmic approaches in industry. This rotating topics course will allow students to take a deep-dive into a sub-discipline within applied AI. Rotating section topics may include robotics, natural language processing, expert systems, constraint satisfaction solvers, evolutionary computation, and others depending on faculty expertise and student interest.

This course will introduce students to practical machine learning and its applications. It will cover both supervised and unsupervised clustering algorithms and their applications in business problems like market segmentation. Students will also become familiar with artificial neural networks and their use in prediction modeling. Software packages and tools related to machine learning algorithms will also be covered.