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**MA 323 Geometric Modeling 4R-0L-4C W (even years)***Prerequisite: MA 113*

Covers some of the mathematical methods for describing physical or virtual objects in computer aided geometric design (CAGD) and computer graphics. Emphasizes methods for curve and surface modeling, and discusses both the underlying geometric concepts and the practical aspects of constructing geometric models of objects. Topics covered include Bezier curves, Hermite curves, B-splines, Bezier patches, subdivision surfaces. In discussing these, ideas from analytic geometry, differential geometry, affine geometry, combinatorial geometry, and projective geometry will be introduced.

**MA 325 Fractals and Chaotic Dynamical Systems 4R-0L-4C S***Prerequisite: CSSE 220 and MA 222*

Emphasis on the mathematical and computer graphics foundations behind fractal images and the relationship between chaotic dynamics and fractal geometry. Self-similar fractals, random fractals with Brownian motion, and fractals generated from dynamical systems. Fractal dimensions. Iterated function systems. Chaos in one-dimensional maps. Controlling chaos. Mandelbrot and Julia sets. Computer graphics. Same as CSSE 325.

**MA 327 Low Dimensional Topology 4R-0L-4C W***Prerequisite: MA 113 or consent of instructor*

An introduction to the topology of one-, two-, and three-dimensional manifolds and its application to other areas of mathematics and science. Topics may include, but are not restricted to, classification of curves and surfaces, Euler characteristic, tiling and coloring theorems, graph embeddings, vector fields, knots and links, and elementary algebraic topology. Intended for science and engineering majors as well as mathematics majors.

**MA 330 Vector Calculus 4R-0L-4C F***Prerequisite: MA 113*

Calculus of vector- valued functions of one and several variables. Topics include differentiation (divergence, gradient and curl of a vector field) and integration (line integrals and surface integrals). Applications of Green's theorem, Stokes' theorem and the divergence theorem to potential theory and/or fluid mechanics will be provided.

**MA 333 Introduction to Parallel Computing 4R-0L-4CS (odd years)***Prerequisite: MA221 and programming experience*

Principles of scientific computation on parallel computers. Algorithms for the solution of linear systems and other scientific computing problems on parallel machines. Course includes a major project on RHIT's parallel cluster.

**MA 336 Boundary Value Problems 4R-0L-4C S***Prerequisite: MA 222*

Introduction to boundary value problems and partial differential equations. Emphasis on boundary values problems that arise from the wave equation, diffusion equation, and Laplace's equation in one, two and three dimensions. Solutions to such boundary value problems will be discussed using Fourier series, numerical techniques, and integral transforms.

**MA 341 Topics in Mathematical Modeling 4R-0L-4C W (even years)***Prerequisite: MA 222 or consent of instructor*

An introduction to techniques of mathematical modeling involved in the analysis of meaningful and practical problems arising in many disciplines including mathematical sciences, operations research, engineering, and the management and life sciences. Topics include creative and empirical model construction, model fitting, models requiring optimization, and modeling dynamic behavior. Student participation in significant individual and group projects will be emphasized.

**MA 348 Continuous Optimization 4R-0L-4C S (even years)***Prerequisite: MA 222*

Optimization of nonlinear functions of real variables: algorithms for univariate optimization; Golden section, parabolic interpolation, hybrid methods; Newton's Method and variations for multivariate functions; conjugate gradients and quasi-Newton methods; line search strategies; penalty functions for constrained optimization; modeling and applications of optimization.

**MA 366 Functions of a Real Variable 4R-0L-4C W***Prerequisite: MA 275 and MA 113*

Calculus of functions of a single variable. A more careful development of the basic concepts of analysis, including sequences, limits, continuity, differentiability, integration, infinite series, power series, Taylor's Theorem, and uniform convergence.

**MA 367 Functions of a Complex Variable 4R-0L-4C S***Prerequisite: MA 221*

Elementary properties of analytic functions including Cauchy's theorem and its consequences, Laurent series, the Residue Theorem, and mapping properties of analytic functions.

**MA 371 Linear Algebra I 4R-0L-4C F, S***Prerequisite: MA 221 or consent of instructor*

Similar to MA373, but with an emphasis on the theory behind matrices and vector spaces. 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. Some proof writing will be required. Those interested in applications of matrices and vector spaces should take MA373. A student cannot take both MA 371 and MA 373 for credit.

**MA 373 Applied Linear Algebra for Engineers 4R-0L-4C F, S***Prerequisite: MA 221 or consent of instructor*

Similar to MA 371, but with emphasis on applications of matrices and vector spaces. 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. Those interested in the theory behind matrices and vector spaces should take MA 371. A student cannot take both MA 371 and MA 373 for credit.

**MA 375 Discrete and Combinatorial Algebra II 4R-0L-4C W, S***Prerequisite: MA 275*

A continuation of MA 275. Relations. An introduction to finite state machines. More advanced enumeration techniques including recurrence relations, generating functions and the principle of inclusion and exclusion.

**MA 376 Abstract Algebra 4R-0L-4C S***Prerequisite: MA 275*

An introduction to modern abstract algebra and algebraic structures. Topics include congruence and modular arithmetic; rings, ideals, and quotient rings; fields, finite fields, and subfields; groups and subgroups; homomorphisms and isomorphisms. Other topics may also be introduced according to time and student interest.

**MA 378 Number Theory 4R-0L-4C S***Prerequisite: consent of instructor*

Divisibility, congruences, prime numbers, factorization algorithms, RSA encryption, solutions of equations in integers, quadratic residues, reciprocity, generating functions, multiplicative and other important functions of elementary number theory. Mathematical conjecture and proof, mathematical induction.

**MA 381 Introduction to Probability with Applications to Statistics 4R-0L-4C F, W, S***Prerequisite: MA 113*

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.

**MA 382 Introduction to Statistics with Probability 4R-0L-4C F***Prerequisite: MA 381*

This is an introductory course in statistical data analysis and mathematical statistics.Topics covered include descriptive statistics, Sampling distributions (including the entral 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.

**MA 383 Engineering Statistics II 4R-0L-4C F***Prerequisite: MA 223 or MA 382*

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.

**MA 385 Quality Methods 4R-0L-4C S***Prerequisite: MA 223, or MA 382*

Introduction to various aspects of statistical quality control and statistical process control to include the following topics: importance of variance reduction and probability concepts influencing product quality and reliability; development and application of control charts (P-charts, NP-charts, C-charts, U-charts, individual's charts, moving range charts, X-bar and R as well as X-bar and S charts); process capability indices (their use and misuse); introduction to acceptance sampling. Other topics to be included as time allows: 6 sigma thinking, gauge reproducibility and repeatability, and total quality management with the philosophies of Deming, Juran, and Crosby. Review of fundamental prerequisite statistics will be included as necessary. Same as CHE 385.

**MA 386 Statistical Programming 4R-0L-4C***Prerequisite: previous programming course and either MA 223 or MA 382*

Database management and statistical analysis using SAS and possibly, R/S+. Topics will include database management (including SQL), data step programming, macro programming, standard data analysis methods (from MA223 or higher level courses), and coding of advanced and/or computationally intense modern algorithms, e.g., bootstrapping and Monte Carlo methods.

**MA 387 Statistical Methods in Six Sigma 4R-0L-4C***Prerequisite: MA 223 or MA 382A 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.

**MA 423 Topics in Geometry 4R-0L-4C (arranged)***Prerequisite: MA 371 or MA 373 or consent of instructor*

An advanced course in geometry. Topics could include from projective geometry, computational geometry, differential geometry, Riemannian geometry, algebraic geometry, Euclidean geometry and non-Euclidean geometry.

**MA 431 Calculus of Variations 4R-0L-4C (arranged)***Prerequisite: MA 330*

Euler-Lagrange and Hamiltonian equations, with possible applications in mechanics, electrostatics, optics, quantum mechanics and elasticity theory. An introduction to "direct methods." Applications will be chosen in accordance with the interest of the students. Both classical and numerical methods have their place in this course.

**MA 433 Numerical Analysis 4R-0L-4C W***Prerequisite: MA 222*

Root-finding, computational matrix algebra, nonlinear optimization, polynomial interpolation, splines, numerical integration, numerical solution of ordinary differential equations. Principles of error analysis and scientific computation. Selection of appropriate algorithms based on the numerical problem and on the software and hardware (such as parallel machines) available.

**MA 434 Topics in Numerical Analysis 4R-0L-4C (arranged)***Prerequisite: MA 433*

An extension of the material presented in MA 433. Topics might include numerical eigenproblems, numerical solution of partial differential equations (finite differences, finite elements, spectral methods), sparse matrices, global optimization, approximation theory.

**MA 436 Introduction to Partial Differential Equations 4R-0L-4C F (even years)***Prerequisite: MA 336*

Partial differential equations, elliptic, hyperbolic, and parabolic equations. Boundary and initial value problems. Separation of variables, special functions. Eigenfunction expansions. Existence and uniqueness of solutions. Sturm-Liouville theory, Green's function.

**MA 439 Mathematical Methods of Image Processing 4R-0L-4C F (odd years)***Prerequisite: MA222*

Mathematical formulation and development of methods used in image processing, especially compression. Vector space models of signals and images, one- and two-dimensional discrete Fourier transforms, the discrete cosine transform, and block transforms. Frequency domain, basis waveforms, and frequency domain representation of signals and images. Convolution and filtering. Filter banks, wavelets and the discrete wavelet transform. Application to Fourier based and wavelet based compression such as the JPEG compression standard. Compression concepts such as scalar quantization and measures of performance.

**MA 444 Deterministic Models in Operations Research 4R-0L-4C W***Prerequisite: MA 221 or MA 371/373*

Formulation of various deterministic problems as mathematical optimization models and the derivation of algorithms to solve them. Optimization models studied include linear programs, integer programs, and various network models. Emphasis on model formulation and algorithm development "from the ground up."

**MA 445 Stochastic Models in Operations Research 4R-0L-4C S (even years)***Prerequisite: MA 223 or MA 381*

Introduction to stochastic mathematical models and techniques that aid in the decision-making process. Topics covered include a review of conditional probability, discrete and continuous Markov chains, Poisson processes, queueing theory (waiting line problems), and reliability.

**MA 446 Combinatorial Optimization 4R-0L-4C S (even years)***Prerequisite: MA 375*

An introduction to graph- and network-based optimization models, including spanning trees, network flow, and matching problems. Focus is on the development of both models for real-world applications and algorithms for their solution.

**MA 460 Topics in Analysis 4R-0L-4C(arranged)***Prerequisite: consent of instructor*

An advanced topics course in analysis. Topic of the course could be advanced topics in real analysis, advanced topics in complex analysis, analysis on manifolds, measure theory or an advanced course in applied analysis (differential equations). May be taken more than once provided topics are different.

**MA 461 Topics in Topology 4R-0L-4C (arranged)***Prerequisite: MA 366 or consent of instructor*

Introduction to selected topics from point-set topology or algebraic topology from a rigorous point of view. Possible topics include metric spaces, general topological spaces, compactness, connectedness, separation axioms, compactification and metrization theorems, homotopy and homology, and covering spaces. Intended for mathematics majors planning to pursue graduate study in mathematics.

**MA 466 Introduction to Functional Analysis 4R-0L-4C (arranged)***Prerequisite: MA 366*

An introduction to the theory of Banach spaces emphasizing properties of Hilbert spaces and linear operators. Special attention will be given to compact operators and integral equations.

**MA 470 Topics in Algebra 4R-0L-4C (arranged)***Prerequisite: consent of instructor*

An advanced topics course in algebra. Topic of the course could be commutative algebra, Galois theory, algebraic geometry, Lie groups and algebras, or other advanced topics in algebra. May be taken more than once provided topics are different.

**MA 471 Linear Algebra II 4R-0L-4C S (even years)***Prerequisite: MA 371 or MA 373*

Continuation of Linear Algebra I. Properties of Hermitian and positive definite matrices and factorization theorems (LU, QR, spectral theorem, SVD). Linear transformations and vector spaces.

**MA 473 Design and Analysis of Algorithms 4R-0L-4C F***Prerequisite: CSSE 230 and MA 375*

Students study techniques for designing algorithms and for analyzing the time and space efficiency of algorithms. The algorithm design techniques include divide-and-conquer, greedy algorithms, dynamic programming, randomized algorithms and parallel algorithms. The algorithm analysis includes computational models, best/average/worst case analysis, and computational complexity (including lower bounds and NP-completeness). Same as CSSE 473.

**MA 474 Theory of Computation 4R-0L-4C W***Prerequisite: CSSE 230 and MA 375*

Students study mathematical models by which to answer three questions: What is a computer? What limits exist on what problems computers can solve? What does it mean for a problem to be hard? Topics include models of computation (including Turing machines), undecidability (including the Halting Problem) and computational complexity (including NP-completeness). Same as CSSE 474.

**MA 475 Topics in Discrete Mathematics 4R-0L-4C S***Prerequisite: MA 375*

An extension of the material presented in MA 275 and 375. Topics may include combinatorial design, Fibonacci numbers, or the Probabilistic Method, among others.

**MA 476 Algebraic Codes 4R-0L-4C S (odd years)***Prerequisite: MA 375 or consent of instructor*

Construction and theory of linear and nonlinear error correcting codes. Generator matrices, parity check matrices, and the dual code. Cyclic codes, quadratic residue codes, BCH codes, Reed-Solomon codes, and derived codes. Weight enumeration and information rate of optimum codes.

**MA 477 Graph Theory 4R-0L-4C S (even years)***Prerequisite: MA 375 or consent of instructor*

An introduction to the theory and applications of directed and undirected graphs. Possible topics include the following: Connectivity, subgraphs, graph isomorphism, Euler trails and circuits, planarity and the theorems of Kuratowski and Euler, Hamilton paths and cycles, graph coloring and chromatic polynomials, matchings, trees with applications to searching and coding, and algorithms dealing with minimal spanning trees, articulation points, and transport networks.

**MA 478 Topics in Number Theory 4R-0L-4C (arranged)***Prerequisite: MA 378 or MA 375 or consent of instructor*

Advanced topics in Number Theory. Topics may include elliptic curve cryptography, the Fermat-Wiles Theorem, elliptic curves, modular forms, p-adic numbers, Galois theory, diophantine approximations, analytic number theory, algebraic number theory. A student may take the course for credit more than once provided the topics are different.

**MA 479 Cryptography 4R-0L-4C S***Prerequisite: CSSE 220 and MA 275*

Introduction to basic ideas of modern cryptography with emphasis on mathematical background and practical implementation. Topics include: the history of cryptography and cryptanalysis, public and private key cryptography, digital signatures, and limitations of modern cryptography. Touches upon some of the societal issues of cryptography (same as CSSE 479).

**MA 480 Topics in Probability or Statistics 4R-0L-4C (arranged)***Prerequisite: consent of instructor*

An advanced course in probability or statistics. Possible topics include (but are not restricted to) reliability, discrete event simulation, multivariate statistics, Bayesian statistics, actuarial science, nonparametric statistics, categorical data analysis, and time series analysis. May be taken more than once provided topics are different.

**MA 481 Mathematical Statistics 4R-0L-4C W (even years)***Prerequisite: MA 382, or MA 381 and consent of instructor*

An introduction to mathematical statistics. Review of distributions of functions of random variables. Moment generating functions. Limiting distributions. Point estimation and sufficient statistics. Fisher information and Rao-Cramer inequality. Theory of statistical tests.

**MA 482 Bioengineering Statistics 4R-0L-4C S***Prerequisite: MA 223 or MA 382*

Hypothesis testing and confidence intervals for two means, two proportions, and two variances. Introduction to analysis of variance to include one factor and two factors (with interaction) designs. Presentation of simple linear and multiple linear regression modeling; development of analysis of contingency table to include logistic regression. Presentation of Log odds ratio as well as several non-parametric techniques of hypothesis testing and construction of non-parametric confidence intervals and correlation coefficients. Review of fundamental prerequisite statistics will be included as necessary. Same as BE 482.

**MA 485 Applied Regression Analysis and Introduction to Time Series 4R-0L-4C W (odd years)***Prerequisite: MA 221 and either MA 223 or MA 382*

Review of simple linear regression; confidence and prediction intervals for estimated values using simple linear regression; introduction to such concepts as model fit, misspecification, multi-collinearity, heterogeneous variances and transformation of both independent and dependent variables; introduction to multiple regression to include polynomial regression; use of dummy variables and diagnostics based on residuals; sequential variable selection to include forward inclusion and backward exclusion of variables; best subset regression; introduction to time series; autocorrelation; moving averages and exponential smoothing.

**MA 487 Design of Experiments 4R-0L-4C W (even years)***Prerequisite: MA 223 or MA 382*

Review of one factor analysis of variance; tests for homogeneity of variance and model assumptions; multiple comparisons, post hoc comparisons, and orthogonal contrasts; two factor analysis of variance (with and without interactions); three factor and higher full factorial designs; analysis of covariance and repeated measures designs; screening designs to include 2 to the k and 3 to the k design; fractional factorial designs; introduction to General Linear Models. Other topics that may be included as time allows: fixed, random, and mixed designs as well as nested designs. Review of fundamental prerequisite statistics will be included as necessary.

**MA 534 Management Science 4R-OL-4C F (even years)***Prerequisite: Senior or graduate standing*

A study of the development and analysis of various mathematical models useful in managerial decision-making. This includes discussions of what models are, how to create them, how they are used, and what insights they provide. Spreadsheets will be used to do much of the computational work. Topics considered include linear, integer, and nonlinear programming, network models, inventory management, project management, and simulation models. Examples from all areas of business and industry will be investigated. We will also investigate how companies are using these techniques to solve current problems. Same as EMGT 534.

**MA 580 Topics in Advanced Probability Theory and Its Applications 4R-0L-4C (arranged)***Prerequisite: MA 381*

Advanced topics in probability theory as well as applications that are not offered in the listed courses.

**MA 581 Topics in Advanced Statistics 4R-0L-4C (arranged)***Prerequisite: MA 223 or MA 381 and consent of instructor*

This course will cover advanced topics in mathematical statistics as well as applied statistics that are not offered in the listed courses.

**MA 590 Graduate Topics in Mathematics Variable credit***Prerequisite: consent of instructor*

This course will cover graduate-level topics in mathematics not offered in listed courses.

**Rose-Hulman Institute of Technology**

5500 Wabash Avenue

Terre Haute, IN 47803

812-877-1511