Georgia Institute of Technology College of Sciences

Sampling distributions, Normal, t, chi-square and F distributions. Moment generating function methods, Bayesian estimation and introduction to hypothesis testing

Simple random walk and the theory of discrete time Markov chains

This course develops in the theme of "Arithmetic congruence, and abstract algebraic structures." There will be a very strong emphasis on theory and proofs.

The fundamentals of graph theory: trees, connectivity, Euler torus, Hamilton cycles, matchings, colorings and Ramsey theory.

This course is a problem oriented introduction to the basic concepts of probability and statistics, providing a foundation for applications and further study.

Elementary combinatorial techniques used in discrete problem solving: counting methods, solving linear recurrences, graph and network models, related algorithms, and combinatorial designs.

This course includes topics on professional development and responsible conduct of research. The course satisfies the GT RCR Academic Policy for Doctoral Students to complete in-person RCR training.

The second of a two course sequence of faculty-directed independent research culminating in the writing of a senior thesis and its presentation.

The first of a two course sequence of faculty-directed independent research culminating in the writing of a senior thesis and its presentation.

An introduction to the Ito stochastic calculus and stochastic differential equations through a development of continuous-time martingales and Markov processes. (1st of two courses in sequence)