Mathematical Foundations of Data Science and Machine Learning

(4 Credits)

This is an advanced topic course for undergraduate students interested in the modern mathematics of data science and machine learning. Tentative topics include dimension reduction and data visualization, the geometry of high dimensional data, and optimization-based data analysis. Topics may change every year to reflect the current research trends. The course requires an excellent understanding of advanced calculus, linear algebra, and probability theory. Programming skills and knowledge in optimization are strongly recommended but not required. Prerequisite: DATS-SHU 234 Mathematical of Statistics (used to be MATH-SHU 234 ). Fulfillment: Math Constrained Math elective or additional Math elective; Honors Math elective; Data Science Concentration in AI.