An exploration of the mathematics of data science. Analysis of the foundations of algorithms currently used in the field. Potential topics to be covered include: machine learning, compressed sensing, clustering, randomized numerical linear algebra, complex networks and random graph models. Students may repeat this course for further credit under a different topic. Prerequisite: MATH 242, MATH 240 or MATH 232 and STAT 270, all with a minimum grade of C-.
An exploration of the mathematics of data science. Analysis of the foundations of algorithms currently used in the field. Potential topics to be covered include: machine learning, compressed sensing, clustering, randomized numerical linear algebra, complex networks and random graph models. Students may repeat this course for further credit under a different topic. Prerequisite: MATH 242, MATH 240 or MATH 232 and STAT 270, all with a minimum grade of C-.
