134 courses with the subject MAT, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
MAT 100Selected Topics1-6
Exploration of a topic (to be determined) not covered by the standard curriculum but of interest to faculty and students in a particular semester.
MAT 108Basic Probability and Statistics for the Liberal Arts I4
An introductory statistics course designed to cover the basics of descriptive and inferential statistics, including: sampling and experimental design, descriptive statistics and plots, confidence intervals, significance tests, and the basics of linear regression. Only available to students registered in the Online Associate in Arts or Bachelors of Professional Studies degree programs.
MAT 109Basic Probability and Statistics for the Liberal Arts II4
The second of two introductory statistics courses designed to cover the basics of descriptive and inferential statistics, including: sampling and experimental design, descriptive statistics and plots, confidence intervals, significance tests, and the basics of linear regression. Only available to students registered in the Online Associate in Arts or Bachelors of Professional Studies degree programs.
Algebraic operations. Linear and quadratic equations, applications. Exponents and logarithms. Credit not given for MAT 112 after receiving a C or better in MAT 183 or above.
MAT 117Foundational Mathematics via Problem Solving I3
One course in two-course sequence. Number concepts and relationships (including concepts of numeration, operations, number theory), probability and statistics. Emphasizing learning through problem solving.
MAT 121Probability and Statistics for the Liberal Arts I4
First in a two-course sequence. Teaches probability and statistics by focusing on data and reasoning. Topics include displaying data, numerical measures of data, elementary probability, discrete distributions, normal distributions, confidence intervals. NOTE: A student cannot receive credit for MAT 121 after completing STT 101 or any MAT course numbered above 180 with a grade of C or better.
MAT 122Probability and Statistics for the Liberal Arts II4
Second in a two-course sequence. Teaches probability and statistics focusing on data and reasoning. Topics include hypothesis testing, linear correlation, linear regression, nonparametric methods, statistical process control, solving liner equations, matrices, Markov chains. NOTE: A student cannot receive credit for MAT 122 after completing any MAT course numbered above 180 with a grade of C or better.
Offered through SUAbroad by educational institution outside the United States. Student registers for the course at the foreign institution and is graded according to that institution's practice. SUAbroad works with the S.U. academic department to assign the appropriate course level, title, and grade for the student's transcript.
Linear equations, matrices, and linear programming. Introduction to mathematics of finance. Discrete probability theory. For students interested in management, finance, economics, or related areas.
In-depth exploration of a problem or problems. Individual independent study upon a plan submitted by the student. Admission by consent of supervising instructor or instructors and the department.
Polynomial, rational, exponential, and logarithmic functions. Analytical trigonometry and trigonometric functions. Emphasis on algebra throughout the course. A student cannot receive credit for MAT 193 after receiving a grade of C or better in any calculus course. Credit cannot be given for both MAT 193 and MAT 194.
Polynomial, rational, exponential, and logarithmic functions. Analytical trigonometry and trigonometric functions. A student cannot receive credit for MAT 194 after receiving a grade of C or better in any calculus course. Credit cannot be given for both MAT 193 and MAT 194.
First of a two-course sequence. For students in fields that emphasize quantitative methods. Probability, design of experiments, sampling theory, introduction of computers for data management, evaluation of models, and estimation of parameters.
Continuation of MAT 221. Further methods of statistical analysis emphasizing statistical reasoning and data analysis using statistical software. Basic concepts of hypothesis testing, estimation and confidence intervals, t-tests and chi-square tests, linear regression, analysis of variance.
Offered through SUAbroad by educational institution outside the United States. Student registers for the course at the foreign institution and is graded according to that institution's practice. SUAbroad works with the SU academic department to assign the appropriate course level, title, and grade for the student's transcript.
One-variable differential and integral calculus. Applications to business and economics. MAT 284 may not be taken for credit after obtaining a grade of C or higher in MAT 285 or MAT 295.
Functions and their graphs, derivatives and their applications, differentiation techniques, the exponential and logarithmic functions, integration. MAT 285 may not be taken for credit after obtaining a grade of C or higher in MAT 284 or MAT 295.
Integration methods and applications, first order differential equations with applications, multivariable differential calculus including optimization. Cannot be taken for credit after obtaining a grade of C or higher in MAT 296.
In-depth exploration of a problem or problems. Individual independent study upon a plan submitted by the student. Admission by consent of supervising instructor or instructors and the department.
Delve more deeply into the material in MAT 295. Solve more challenging problems. For prospective mathematics majors and others with strong background and interest in mathematics.
Delve more deeply into the material in MAT 296. Solve more challenging problems. For prospective mathematics majors and others with strong background and interest in mathematics.
Limits, continuity, derivatives, related rates, maxima and minima of functions, optimization problems, L'Hospital's Rule, integration, the Fundamental Theorem of Calculus, integration by substitution. For science majors.
Linear equations, n-dimensional euclidean space, linear independence, bases, linear transformations, matrices and determinants, eigenvalues and eigenvectors. Geometric aspects.
Principles of symbolic logic, set theory, basic function theory, relations, and cardinality. Emphasis on developing proof-writing skills, including mathematical induction. Credit cannot be given for both MAT 375 and CIS 375.
Offered through SUAbroad by educational institution outside the United States. Student registers for the course at the foreign institution and is graded according to that institution's practice. SUAbroad works with the S.U. academic department to assign the appropriate course level, title, and grade for the student's transcript.
Exploration of a problem, or problems, in depth. Individual independent study upon a plan submitted by the student. Admission by consent of supervising instructor(s) and the department.
Delve more deeply into the material in MAT 397. Solve more challenging problems. For prospective mathematics majors and others with strong background and interest in mathematics.
Vectors and geometry of space, vector functions, functions of more than one variable, partial derivatives, multiple integrals, line and surface integrals, Green¿s, Stokes¿, and Divergence Theorems.
Introduction to the foundations of calculus covering topics from the following: the real number system, functions, limits, sequences, infinite series, continuity, and uniform continuity. Enrollment limited to mathematics majors.
MAT 414Introduction to Ordinary Differential Equations3
First order differential equations. Second order linear differential equations. Power series solutions. Bessel¿s equations, Laplace transforms. Systems of first order differential equations. Applications.
Sample spaces, counting, random variables and their distributions, expected value, central limit theorem. Estimation and confidence intervals, hypothesis tests, analysis of variance and regression. MAT 421 does not count towards the Mathematics or Applied Mathematics major or minor. Cannot be taken for credit after successfully completing MAT 521.
Characteristics of data, data preparation and reformatting, accessing data from various sources, programming principles for statistical computing, data summary and visualization, statistical study design aspects of sampling and randomization, introduction to statistical simulation.
Participation in a discipline or subject related experience. Student must be evaluated by written or oral reports or an examination. Permission in advance with the consent of the department chairperson, instructor, and dean. Limited to those in good academic standing.
Offered through SUAbroad by educational institution outside the United States. Student registers for the course at the foreign institution and is graded according to that institution's practice. SUAbroad works with the S.U. academic department to assign the appropriate course level, title, and grade for the student's transcript.
MAT 485Differential Equations and Matrix Algebra for Engineers3
Solution of ordinary differential equations, including series methods. Vector spaces, matrix algebra, rank, linear systems, eigenvalues and eigenvectors. MAT 485 may not be taken for credit after receiving a grade of C or higher in both MAT 331 and MAT 414. Does not count toward mathematics major.
In-depth exploration of a problem or problems. Individual independent study upon a plan submitted by the student. Admission by consent of supervising instructor or instructors and the department.
Fundamental methods for data science, such as regression, linear discriminant analysis, k-nearest neighbors, support vector machine, k-means, principal component analysis, and nonlinear dimension reduction. Performance evaluation and model selection. Additional work required of graduate students.
Matrix methods required for data analysis with an emphasis on applications and using software. Matrix norms, orthogonality, eigendecomposition, SVD, LS, QRD, LDA, PCA. Not for math majors or minors. Additional work required of graduate students.
Real-number system, set theory and elementary topological properties of the real line, continuity and differentiability, sequences and series, uniform convergence, Riemann integration, and improper integrals.
Complex number system and its arithmetic, geometric representation. Linear transformations. Analytic functions and the Cauchy-Riemann equations. Integration and Cauchy's theorem, Taylor and Laurent series, singularities, poles, and residues. Applications.
Orthogonal functions, Fourier series, Fourier transforms-continuous and discrete, Haar wavelets and multiresolution analysis, applications to signal processing. Additional work required of graduate students.
Algebra of sets. Probability in finite sample spaces. Binomial and multinomial coefficients. Random variables. Expected value and standard deviation. Density functions. Statistical applications.
Statistical methods (such as hypothesis testing, parameter estimation, regression, ANOVA, sampling, experimental design) required for data science. Emphasis on applications and using software. Additional work required for graduate students.
Concept, theory, methods, and applications of simple linear regression, multiple linear regression, and logistic regression models; Parameter estimation and testing, Prediction, Regression diagnostics and model adequacy checking, Variable selection and model building.
Estimation and confidence intervals. Normal distribution and central limit theorem. Testing hypotheses, chi-square, t, and F distributions. Least squares, regression, and correlation.
MAT 527Analysis of Variance and Experimental Design3
One-way, two-way, and multi-way analysis of variance, analysis of covariance, random/mixed effect models, repeated measure analysis, and special designs such as a randomized block design, an incomplete block design, Latin square, and a nested design.
Applied probability focusing on distributions for actuarial applications. Conditional expectation. Moment generating functions. Limit theorems. Loss and Survival models. Parametric and Non-Parametric estimation. Model assessment. Benefit reserves and risk measures. Additional work required of graduate students.
Concepts of prior and posterior, Bayesian inference for binomial and Poisson distributions, Monte Carlo approximation, Bayesian inference for normal distribution, Gibbs sampling, hierarchical models, multivariate normal distribution, Metropolis-Hastings algorithms, linear regression models.
Abstract vector spaces and inner product spaces, linear transformations and linear operators, eigenvalues and diagonalization. Primarily for mathematics majors.
Factorization of matrices, eigenvalues and eigenvectors, orthogonality. Applications of matrices to such topics as least-squares approximation, fast Fourier transform, difference and differential equations, linear programming, networks, game theory.
Prime numbers, greatest common divisors, congruences. Euler's function, Fermat's theorem, primitive roots, indices, quadratic residues, Legendre and Jacobi symbols, and the quadratic reciprocity law.
Synthetic projective geometries. Coordinate systems for projective spaces. Algebraic representation of projective transformations; euclidean, non-euclidean, and affine geometries as real cases of projective geometry.
Theory of curves in three-dimensional space, including Frenet's formula, Gaussian and mean curvature, geodesics, developable surfaces, special conformal mappings.
Offered through SUAbroad by educational institution outside the United States. Student registers for the course at the foreign institution and is graded according to that institution's practice. SUAbroad works with the S.U. academic department to assign the appropriate course level, title, and grade for the student's transcript.
Approximation methods for solution of nonlinear equations. Interpolation problems. Numerical integration. Solution of ordinary differential equations. Error analysis and writing computer programs. Primarily for mathematics and engineering students.
Exploration of a problem, or problems, in depth. Individual independent study upon a plan submitted by the student. Admission by consent of supervising instructor(s) and the department.
Mathematical concepts in their historical perspective. Character and contributions of the great mathematicians and relation of mathematics to other sciences.
In-depth investigation of one or more statistical topics, applications of statistical methods and tools, real-world data analysis project using software, comprehensive presentation of project findings.
Calculus of probabilities, univariate and multivariate random variables and distribution functions, expectations and variance, conditional distributions, transformations of random variables, characteristic functions, basic limit theorems including Borel-Cantelli, Khinchin, Lindeberg- Feller.
Point and interval estimation, consistent, efficient, and sufficient statistics, Rao-Blackwellization, hypothesis testing, brief treatment of ranking and selection, decision theory.
MAT 653Statistical Simulation and Nonstandard Data Analysis3
Simulation and Monte Carlo techniques appropriate where statistical theory does not yet provide a solution. Design and analysis of experiments under nonstandard conditions.
Topological spaces, continuous mappings, compactness, connectedness, path connectedness, separation axioms, metric spaces, quotient spaces, CW complexes, the fundamental group, and the classification of 2-dimensional manifolds.
Participation in a discipline or subject related experience. Student must be evaluated by written or oral reports or an examination. Permission in advance with the consent of the department chairperson, instructor, and dean. Limited to those in good academic standing.
Solution of linear equations. Norms and conditioning. Calculation of eigenvalues and eigenvectors. Least squares approximation and orthogonal functions. Error analysis and writing computer programs.
Mathematical model building, dimensional analysis, scaling, and perturbation theory. Models selected from the natural and social sciences according to the interests of instructor and students. Examples are: planetary orbits, fluid flow, isomers in organic chemistry, biological competition, biochemical kinetics, and physiological flow.
In-depth exploration of a problem or problems. Individual independent study upon a plan submitted by the student. Admission by consent of supervising instructor or instructors and the department.
Fundamental methods for data science, such as regression, linear discriminant analysis, k-nearest neighbors, support vector machine, k-means, principal component analysis, and nonlinear dimension reduction. Performance evaluation and model selection. Additional work required of graduate students.
Measure and integration, including basic theorems on integration and differentiation of sequences of functions; modes of convergence, product measures.
Norms, seminorms, and inner products on linear spaces. Standard theorems on linear functionals and operations. Dual spaces and weak topologies, classical spaces and their duals. Applications.
Existence theorems for ordinary differential equations, linear differential equations and systems, Euler variational equations, typical Cauchy and boundary-value problems for partial differential equations.
Cauchy theory, power series, analytic continuation, entire functions, the residue theorem, contour integration, maximum modulus theorem and applications, conformal representation. Dirichlet series, special functions.
Review of discrete and continuous probability. Introduction to measure-theoretic probability and its applications, such as the construction of Brownian motion, the definition of general conditional expectations, and the convergence theory of random variables (e.g., laws of large numbers and central limit theorems).
Localization, primary decomposition, and dimension theory; Nullstellensatz; Artin-Rees lemma and completion; integral and flat extensions; Koszul complex, Cohen-Macaulay and regular rings.
The course covers representations of finite groups and finite-dimensional algebras. Topics will come from: ordinary and modular representations of finite groups, Auslander-Reiten theory, representations of quivers, Koszul algebras, Hopf algebras and Frobenius algebras.
The study of the zeros of polynomials. Classical algebraic varieties in affine and projective space, followed by introduction to modern theory of sheaves, schemes, and cohomology.
MAT 752Statistical Ranking, Selection, and Multiple Comparisons3
Statistical selection of the best category or population. Preference-zone and subset formulations. Multivariate preferences and populations. Applications. Recent developments, including Multiple Comparisons with the Best (MCB) and the Heteroscedastic Method (HM).
General sequential decision problems, sequential probability ratio test, sequential test among three hypotheses, sequential estimation, optimal stopping, Wald's identity. Generalized SPRT's, Cox's theorem, sequential regression, functional equations, dynamic programming, sequential choice of experiments.
Multivariate normal distribution, conditional densities, partial correlation, multiple correlation, regression coefficients, maximum likelihood estimates, Hotelling's statistic, Wishart distribution, tests of hypotheses, and linear discriminant functions.
Overview of nonparametric statistics, empirical CDF, statistical functionals, density and probability function estimation, nonparametric kernel methods, nonparametric regression, conditional density estimation, bootstrap, partial linear models, single index models, and other advanced topics.
Topics in machine learning from statistical and probabilistic perspective: prediction errors and cross validation, bias-variance trade-off, regularized regression, variable selection, nonparametric regression and classification, bootstrap, trees-based methods, neural network, and other advanced topics.
Fundamental group covering spaces, chain complexes, simplicial or singular homology and cohomology theory, exact sequences, and the Eilenberg-Steenrod axioms.
Differential manifolds, tensor fields and mappings, differential forms and Stokes's theorem, affine connections, exponential mapping, covariant differentiation, torsion and curvature tensors, Riemannian connections, complete Riemannian manifolds, other modern topics.
Newton methods, interior point methods, proximal point algorithms, alternating direction method of multipliers, coordinate descent method, and stochastic/randomized algorithms.
Topics in real variables and measure theory, such as differentiation theory in euclidean and abstract spaces, generalized derivatives and integrals, ergodic theory, martingales, surface area.
Abstract integration, Radon-Nikodym theorem. Representation of set functions by integrals. Ergodic theorems. Duality. Weak topologies, convex sets, and extreme points. Elements of spectral theory.
First-order linear equations. Hamilton, Jacobi, and Lie transformations. Classifications of second-order linear equations. Boundary- and initial-value problems. Sturm-Liouville problems and connections with integral equations. Nonlinear equations.
Classification and examples of regular integral equations. Fredholm's theorems, Hilbert-Schmidt theory. Applications to differential equations. Nonlinear integral equations. Connections with general functional analysis.
Exploration of a problem, or problems, in depth. Individual independent study upon a plan submitted by the student. Admission by consent of supervising instructor(s) and the department.
Research work on a doctoral dissertation, under supervision of some member of the graduate staff. Credit depends on amount of time devoted to the work; course may be repeated up to a maximum of 30 credits.
