Reading courses for upper division students of high standing.

Reading courses for upper division students of high standing.

Reading courses for upper division students of high standing.

Topics selected from stochastic models, decision making under uncertainty, inventory models with random demand, waiting time models and decision problems.

This course introduces deep learning with its mathematical foundation, algorithms, and programming tools. Students will learn the basics of deep learning algorithms and gain related foundational knowledge in linear algebra, optimization, and probability and information theory. The students will also get programming experiences in building deep neural networks for some real-world data problems.

This course introduces financial mathematical models using discrete and continuous stochastic processes. Students will learn to construct and analyze models used in pricing financial options and futures, and other financial contracts. The students will also learn how to construct an optimal portfolio of stocks given various criteria.

Elementary complex variable theory with applications, Complex field, analytic functions. Cauchy theorem, power series, residue theory. Prereq: MA 213 or consent of the instructor.

A continuation of MA 471G to functions of several variables. A careful and rigorous investigation of the extensions of the concepts of the one variable calculus to n-dimensions.

Floating point arithmetic. Direct methods for the solutions of systems of linear algebraic equations. Polynomial and piecewise polynomial approximation, orthogonal polynomials. Numerical integration: Newton Cotes formulas and Gaussian quadrature. Basic methods for initial value problems for ordinary differential equations. The emphasis throughout is on the under- standing and use of software packages for the solution of commonly occurring problems in science and engineering.

Reading course for graduate students in mathematics. May be repeated to a maximum of nine credits.