Fall 2012 Mathematics Electives
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MA275 Discrete and Combinatorial Algebra I, Periods 2, 6, 9 MTRF
- Ralph Grimaldi, John Rickert and Allen Broughton
An introduction to enumeration and discrete structures. Elementary mathematical
logic. Permutations, combinations and related concepts. Set theory, relations
and functions on finite sets. Mathematical induction. This course is part
of a two quarter sequence and is especially helpful in supporting the
understanding of computer science concepts. (required for CS, SE, CPE and MA).
MA330 Vector Calculus, Period 6 MTRF - Bill Butske
Calculus of functions of several variables. Topics include differentiation
(divergence, gradient, curl) and integration (line, and surface integrals).
Green's theorem, Stokes' theorem, and the divergence theorem are also covered.
This course in vector calculus is an excellent companion course to the study
of electromagnetic fields.
MA332 Introduction to Computational Science Period 3 MTRF - Joe Eichholz
An introduction to Computational Science using Matlab. Floating point arithmetic, Matlab programming, solution of nonlinear equations, interpolation, least squares problems, numerical differentiation and integration, solution of linear systems. For more information see this webpage.
Value Problems (4 credits)
Period 9 MTRF - Dave Goulet
Prerequisite: MA211 and MA212
MA336 is at the heart of classical applied mathematics. Learning to speak
the language of boundary value problems allows you to understand aspects of
wave motion, heat transfer, potential theory, and electromagnetics.
MA371 Linear Algebra I, Period 8 MTRF - TBA
Prerequisite: MA212 or permission of instructor
Systems of linear equations, Gaussian elimination, and the LU decomposition
of a matrix. Projections, least squares approximations, and the Gram-Schmidt
process. Eigenvalues and eigenvectors of a matrix. The diagonalization
theorem. The singular value decomposition of a matrix. Introduction
to vector spaces. A student cannot take both MA 371 and MA 373 for credit.
MA381 Introduction to Probability with Applications to Statistics, (4 credits)
Periods 4 and 5 MTRF
- Diane Evans
Introduction to probability theory; axioms of probability, sample spaces, and
probability laws (including conditional probabilities). Univariate random variables
(discrete and continuous) and their expectations including these distributions:
binomial, Poisson, geometric, uniform, exponential, and normal. Introduction
to moment generating functions. Introduction to jointly distributed random variables.
Univariate and joint transformations of random variables. The distribution of
linear combinations of random variables and an introduction to the Central Limit
Theorem. Applications of probability to statistics.
to Statistics with Probability (4 credits)
Period 7 MTRF - Dave Rader
Prerequisite: MA 381
This is an introductory course in statistical data analysis and mathematical
statistics. Topics covered include descriptive statistics, Sampling distributions
(including the Central Limit Theorem), point estimation, Hypothesis testing and
confidence intervals for both one and two populations, linear regression, and
analysis of variance. Emphasis will be placed on both data analysis and mathematical
derivations of statistical techniques. A computer package will be used for statistical
analysis and simulation. Experimental data from a variety of fields of interest
will also be used to illustrate statistical concepts and facilitate the development
of the student's statistical thinking. A student cannot take both MA 223 and
MA 382 for credit.
MA386 Statistical Programming (4 credits)
Period 5 MTRF - Mark Inlow
Prerequisite: MA223 or MA382
For further information contact the instructor Professor Inlow.
Please note that this is only offered every second year.
MA387 Statistical Methods in Six Sigma (4 credits)
Period 8 MTRF - Diane Evans
Prerequisite: MA223 or MA382
A course on statistical methods used in the Six Sigma /DMAIC (Define, Measure, Analyze, Improve, Control) paradigm. Topics will include, but are not limited to, gauge repeatability and reproducibility, control charts, regression, design of experiments, and response surface optimization.
MA390 Introduction to the FFT (1 credit)
Period 5 W - Elton Graves
Prerequisite: consent of instructor
For further information contact the instructor Professor Graves.
MA436 Introduction to Partial Differential
Equations (4 credits)
Period 6 MTRF - Dave Finn
For further information contact see this webpage or
email the instructor Dave Finn.
Methods in Image Processing
Period 4 MTRF - Allen Broughton
Mathematical methods of image processing such as filtering, filter banks, Fourier & discrete
cosine transforms and wavelet based analysis. Applications such as image compression
and denoising will be studied.
For more info see this page.
and Analysis of Algorithms, Period 3 and 4 - Claude Anderson
Prerequisites: CSSE 230 and MA 375
One of two math based theoretical computer science courses that may be taken
as an MA course or a CSSE course. Taught in alternate years by MA and CSSE professors.
MA491 Intro to Mathematical Modeling (2
Period 5 TR - Al Holder
Prerequisite: Senior standing or consent of instructor
This courses is intended for math majors ans double majors who will be doing a senior project. The projects will have some
type of math application and MA491 is designed to give the students some background and practice in mathematical
Any questions? Just send me mail. firstname.lastname@example.org
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