WSS NEWS

December, 2005

Spring 2006 Graduate Courses
Applied and Computational Mathematics Program , Johns Hopkins University

The Applied and Computational Mathematics (ACM) Program at Johns Hopkins University will offer the graduate courses listed below in the spring semester (23 January 2006 to 6 May 2006) at locations in the Baltimore-Washington area.

Subject to meeting admission criteria, a non-degree candidate may register as a special student to take one or more courses to enhance mathematical and statistical skills. These courses are scheduled at times convenient for the working adult. Registration and general information is at http://www.epp.jhu.edu. Information specific to the ACM Program is at http://www.epp.jhu.edu/05_06_catalog/acm.html. For further information related to academic requirements and course content, please contact Dr. James Spall, Program Chair, at james.spall@jhuapl.edu or 240-228-4960.

625.403 Statistical Methods and Data Analysis
Instructor: Sue-Jane Wang
Time and location: Tuesdays, 4:30 -7:10PM, Montgomery County Center (Rockville, MD)

This course introduces commonly used statistical techniques. The intent of this course is to provide an understanding of statistical techniques and a tool box of methodologies. Statistical software is used so students can apply statistical methodology to practical problems in the workplace. Intuitive developments and practical use of the techniques are emphasized rather than theorem/proof developments. Topics include the basic laws of probability and descriptive statistics, conditional probability, random variables, expectation, discrete and continuous probability models, joint and sampling distributions, hypothesis testing, point estimation, confidence intervals, contingency tables, logistic regression, and linear and multiple regression.

Prerequisite: Multivariate calculus.

625.404 Ordinary Differential Equations
Instructor: Ronald Farris
Time and location: Thursdays, 7:15 -10:00PM, Applied Physics Laboratory (southern Howard County)

Topics discussed throughout the course include methods of solving first-order differential equations, existence and uniqueness theorems, second-order linear equations, power series solutions, higher-order linear equations, systems of equations, non-linear equations, Sturm-Liouville theory, and applications.

Prerequisite: Two or more terms of calculus.

625.417 Applied Combinatorics and Discrete Mathematics
Instructor: J. Miller Whisnant
Time and location: Tuesdays, 4:30 -7:10PM, Applied Physics Laboratory (southern Howard County)

Combinatorics and discrete mathematics are increasingly important fields of mathematics because of their extensive applications in computer science, statistics, operations research, and engineering. The purpose of this course is to teach students to model, analyze, and solve combinatorial and discrete mathematical problems. Topics include elements of graph theory, graph coloring and covering circuits, the pigeonhole principle, counting methods, generating functions, recurrence relations and their solution, and the inclusion-exclusion formula. Emphasis is on the application of the methods to problem solving.

Prerequisite: Two or more terms of calculus.

625.438 Neural Networks
Instructor: J. Miller Whisnant
Time and location: Mondays, 4:30 -7:10PM, Applied Physics Laboratory (southern Howard County)

This course provides an introduction to concepts in neural networks and connectionist models. Topics include parallel distributed processing, learning algorithms, and applications. Specific networks discussed include Hopfield networks, bidirectional associative memories, perceptrons, feedforward networks with back propagation, and competitive learning networks, including self-organizing and Grossberg networks. Software for some networks is provided.

Prerequisite: Multivariate calculus.

625.461 Applied Linear Statistical Models
Instructor: Allan D. McQuarrie
Time and location: Wednesdays, 7:15 -10:00PM, Applied Physics Laboratory (southern Howard County)

Linear statistical models for regression, analysis of variance, and experimental design are widely used today in engineering, business administration, economics, and the social, health, and biological sciences. This course will provide the sound understanding of both the underlying theory and the practical problems required for the successful application of these models. The topics covered are multiple linear regression, time series models, analysis of variance for fixed and random effects and nested and crossed treatments, and experimental design, especially factorial designs.

Prerequisites: One semester of statistics (such as 625.403), multivariate calculus, and linear algebra

625.710 Fourier Analysis with Applications to Signal Processing and Differential Equations
Instructor: Richard Spencer
Time and location: Mondays, 7:15 -10:00PM, Dorsey Center (near BWI Airport)

This is an applied course covering the theory and applications of Fourier analysis, including the Fourier transform, the Fourier series and the discrete Fourier transform. Applications in signal processing will be emphasized, including the sampling theorem and aliasing, convolution theorems, spectral analysis, and the imaging point spread function. Further applications, also incorporating the Laplace transform, will be taken from studies of differential equations arising in engineering and physics.

Prerequisites: Some familiarity with complex variables, differential equations, and linear algebra.

625.722 Probability and Stochastic Processes II
Instructor: Mostafa Aminzadeh
Time and location: Mondays, 4:30 -7:10PM, Applied Physics Laboratory (southern Howard County)

This course is an introduction to the theory of discrete-time stochastic processes. Emphasis in the this course is given to Poisson processes, renewal theory, renewal reward process, Markov chains, continuous-time Markov chains, birth and death process, Brownian motion, and random walks.

Prerequisites: Differential equations and 625.721 Probability and Stochastic Process I or equivalent.

625.734 Queuing Theory with Applications to Computer Science
Instructor: Eric Blair
Time and location: Thursdays, 4:30 -7:10PM, Applied Physics Laboratory (southern Howard County)

Queues are a ubiquitous part of everyday life; common examples are supermarket checkout stations, help desks call centers, manufacturing assembly lines, wireless communication networks, and multi-tasking computers. Queuing theory provides a rich and useful set of mathematical models for the analysis and design of service process for which there is contention for shared resources. This course explores both theory and application of fundamental and advanced models in this field. Fundamental models include single and multiple server Markov queues, bulk arrival and bulk service processes, and priority queues. Applications emphasize communication networks and computer operations, but may include examples from transportation, manufacturing, and the service industry. Advanced topics may vary.

Prerequisite: Multivariate calculus and knowledge of probability.

625.251 Applied Mathematics II (this course is not offered for graduate credit)
Instructor: James D’Archangelo
Time and location: Wednesdays, 7:15 -10:00PM, Applied Physics Laboratory (southern Howard County)

(This course is a companion to 625.250) Topics include ordinary differential equations, Fourier series and integrals, the Laplace transformation, Bessel functions and Legendre polynomials, and an introduction to partial differential equations.

Prerequisites: Differential and integral calculus. Students with no experience in linear algebra may find it helpful to take 625.250 Applied Mathematics I first.