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.


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