The history of mathematics from ancient to modern times. The mathematicians, their times, their problems, and their tools. Major emphasis on the development of geometry, algebra, and calculus.
Occasional Interims
Previously: MATH 330
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
A review of Euclidean geometry, an examination of deficiencies in Euclidean geometry, and an introduction to non-Euclidean geometrics. Axiomatic structure and methods of proof are emphasized.
Occasional Interims
Previously: MATH 335
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
Point Estimation, interval estimation, hypothesis testing, one factor analysis of variance (ANOVA) and two-way, Chi-Square Goodness of Fit Tests, Contingency Tables.
Occasionally
Previously: MATH 316
Previously: MATH 397