Topics to be selected from counting techniques, mathematical logic, set theory, data structures, graph theory, trees, directed graphs, algebraic structures, Boolean algebra, lattices, and optimization of discrete processes.
Every Spring
Previously: MATH 320
This course explores the mathematical foundations of algorithms used in the field of Data Science, typically taken after a course in mathematical statistics. It includes the study of classification and regression techniques, robust regression, decision trees, support vector machines, neural networks, cross-validation techniques, forecasting models, and Topological data analysis. The course includes a data-driven project that requires the student to propose a question and gather, clean, and analyze data to present a response to a real-world problem.
Occasionally
Previously: MATH 327
The specific topics of the course include combinatorics, basic probability, discrete and cont. random variables, probability distributions (emphasis on Normal distribution), multivariate dist., expected values, conditional probability, independence, Moment generating functions, central limit theorem.
Every Fall
Previously: MATH 315