Mathematics of Artificial Intelligence I

Catalog description

This course introduces the fundamental ideas of statistical learning and data-driven modeling. Emphasizing intuition and practical understanding, the course explores how data can be used to make predictions, uncover patterns, and inform decision-making across disciplines. Topics include regression and classification methods, model assessment, resampling techniques, and essential concepts such as the bias–variance tradeoff and overfitting. Students will gain hands-on experience applying these methods using modern statistical and data science software tools. The focus is on interpretation, visualization, and communication of results rather than complex mathematical derivations, preparing students for more advanced study in data science, Machine Learning (ML), and Artificial Intelligence (AI). CS 446 is cross-listed with MS 446 , and only one course can be taken for credit.