Spring 2016

## Statistical Estimation

Basic theories of statistical estimation, including optimal estimation in finite samples and asymptotically optimal estimation. A careful mathematical treatment of the primary techniques of estimation utilized by statisticians.

## Probability II

Develops the probability basis requisite in modern statistical theories and stochastic processes. (2nd of two courses)

## Advanced Classical Probability Theory

Classical introduction to probability theory including expectation, notions of convergence, laws of large numbers, independence, large deviations, conditional expectation, martingales and Markov chains.

## Algebra II

Graduate level linear and abstract algebra including rings, fields, modules, some algebraic number theory and Galois theory. (2nd of two courses)

## Calculus III for Computer Science

Topics in linear algebra and multivariate calculus and their applications in optimization and numerical methods, including curve fitting, interpolation, and numerical differentiation and integration.

## Survey of Calculus

Functions, the derivative, applications of the derivative, techniques of differentiation, integration, applications of integration to probability and statistics, multidimensional calculus.

## Finite Mathematics

Linear equations, matrices, linear programming, sets and counting, probability and statistics.

## Calculus II

See MATH 1552, 1553, 1554, 1564. Concludes the treatment of single variable calculus, and begins linear algebra; the linear basis of the multivariable theory. The first 1/3 of this course covers more advanced single variable calculus. The remaining 2/3 is an introduction to linear algebra, the theory of linear equations in several variables.

## Honors Multivariable Calculus

The topics covered parallel those of MATH 2551 with a somewhat more intensive and rigorous treatment.

## Differential Equations

Methods for obtaining numerical and analytic solutions of elementary differential equations. Applications are also discussed with an emphasis on modeling.