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MA275 Discrete and Combinatorial Algebra I, Periods 3, 4, 7  MTRF - Ralph Grimaldi, Leane Holder and Tom Langley
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 courses is part of a three quarter sequence and is especially helpful in supporting the the understanding of computer science concepts. (required for CS, CPE and MA).

MA330 Vector Calculus, Period 5 MTRF - Steve Galinaitis
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 3 MTRF - Cara Brooks
Prerequisite: MA22
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.  See course description page for more details.

MA351 Problem Solving Seminar, Period 7 W (1 credit) - Steve Carlson
Prerequisite: Consent of instructor
An exposure to mathematical problems varying widely in both difficulty and content. Students will be expected to participate actively, not only in the solution process itself but also in the presentation of finished work, both orally and in writing. A student may earn a maximum of six credits in MA351-6. For more info see this page.

MA371 Linear Algebra I, Period 4 MTRF - Tanya Jajacay
Prerequisite:  MA221 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, 6 MTRF - Diane Evans, Yosi Shibberu
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 3 MTRF - Mike Devasher
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.

MA383 Engineering Statistics II  (4 credits)
Period 4 MTRF - Mark Inlow
Prerequisite: MA223 or MA382.
As suggested by the name, this course is a follow on to MA223, going into greater depth the fundamental engineering statistics as well as some new topics. Hypothesis testing, confidence intervals, sample size determination, and power calculations for means and proportions; two factor analysis of variance (with and without interactions); analysis of several proportions;  confidence and prediction intervals for estimated values using simple linear regression; Pearson (linear) correlation coefficient; introduction to multiple regression to include polynomial regression; review of fundamental prerequisite statistics will be included as necessary.

MA439 Mathematical Methods in Image Processing (4 credits)
Period 6 MTRF - Kurt Bryan
Prerequisite: MA222
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.

MA450 Math Seminar TBA (1 credit)  Josh Holden
Prerequisite: none
This new is course is designed to encourage students to attend department mathematics seminars and make a presentation of their own. Students  must attend nine seminars and make presentation of their own on a topic chosen in consultation with the instructor. A student will be allowed two consecutive quarters to complete all requirements.

MA473 Design and Analysis of Algorithms, Period 9 - Josh Holden
Prerequisites: CSSE 230 and MA 375
One of two math based theoretical computer science courses that may be taken as a math course or a CSSE course. Taught in alternate years by MA and CSSE professors. Email Josh Holden for more details.

MA490 Mathematical Logic (4 credits)
Period 5 MTRF - Mike Wollowski
Crosslisted with CSSE490-01 For more info email the insructor Mike Wollowski.

MA491 Intro to Mathematical Modeling (2 credits)
Period 4 MR - Allen Broughton
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. For more info see this page.