Click on the course number to get a catalogue description.

MA275 Discrete and Combinatorial Algebra I, Periods 2, 6, 9  MTRF - Ralph Grimaldi, John Rickert and Allen Broughton
Prerequisite: MA112
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
Prerequisite: MA113
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
Prerequisite: MA212
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.

MA336 Boundary 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
Prerequisite: MA113
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.

MA382 Introduction 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
Prerequisite: MA330
For further information contact see this webpage or email the instructor Dave Finn.

MA439 Mathematical Methods in Image Processing
Period 4 MTRF - Allen Broughton
Prerequisite: MA212
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.

MA473 Design 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 credits)
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 modeling.