2022-2023 Course List

This course is an introduction to mathematical concepts needed in computer science, including sets, relations and functions, propositional logic, proof techniques, recurrence relations, graphs and trees, and discrete probability. This course is not intended for students pursuing a degree in mathematics.

Prerequisites:

MATH 121 with “C” (2.0) or better or consent.

Logic, proof techniques, set theory, relations, functions, cardinality, operations, and an introduction to mathematical structures and number theory.

Prerequisites:

MATH 122 with “C” (2.0) or better or consent.

Goal Areas:

GE-02

This class provides MAX scholars with an opportunity to explore a set of topics related to achieving success in academic, professional and personal realms. Speakers will include faculty, graduate students, visiting researchers, and industry members as well as student participants. Cannot be used towards a math major.

Prerequisites:

Recipient of a MAX scholarship or instructor consent.

Limits, sequences, continuity, and differentiation of a real valued function of a real variable.

Prerequisites:

MATH 223 and MATH 290 with “C” (2.0) or better or consent

This course presents the theory, computations, and applications of first and second order differential equations and two-dimensional systems.

Prerequisites:

MATH 122 with “C” (2.0) or better or consent

This course covers several geometric systems including Euclidean, non-Euclidean, transformational and projective. Other topics studied are topological properties and the relationship between coordinate and synthetic geometry.

Prerequisites:

MATH 290 with “C” (2.0) or better or consent

An introduction to the theory of groups and rings; including polynomial rings, homomorphisms, isomorphisms, and concepts of normal subgroups, ideals, quotient groups, and quotient rings.

Prerequisites:

MATH 290 with “C” (2.0) or better or consent

A calculus based introduction to probability and statistics. Topics include probability, random variables, probability distributions (discrete and continuous), joint probability distributions (discrete and continuous), statistical inference (both estimation and hypothesis testing), confidence intervals for distribution of parameters and their functions, sample size determinations, analysis of variance, regression, and correlation. This course meets the needs of the practitioner and the person who plans further study in statistics.

Prerequisites:

MATH 122 with “C” (2.0) or better or consent

MATH 375 Introduction to Discrete Mathematics (4 credits)An introduction to the concepts fundamental to the analysis of algorithms and their realization. Topics will include combinatorics, generating functions, recurrence relations, graph theory, and networks.

Prerequisites:

MATH 247 and MATH 290 with grade of “C” (2.0) or higher.

A continuation of the topics from MATH 280. The major focus of the course is understanding and analyzing algorithms, including proving that algorithms perform correctly. Topics include modular arithmetic, counting problems, sorting algorithms and constructions on graphs. This course is not intended for students pursuing a major degree in mathematics.

Prerequisites:

MATH 247 and MATH 280 with a grade of “C” (2.0) or better

Curricular Practical Training: Co-Operative Experience is a zero-credit full-time practical training experience for one semester and an adjacent fall or spring term. Special rules apply to preserve full-time student status. Please contact an advisor in your program for complete information.

Prerequisites:

At least 60 credits earned; in good standing; instructor permission; co-op contract; other prerequisites may also apply.

An introduction to topological spaces and their fundamental properties such as compactness, connectedness, separation properties and countability properties. Continuous functions between topological spaces and common examples of topological spaces are also discussed.

Prerequisites:

MATH 290 with grade of “C” (2.0) or higher. 

Algebra and geometry of complex numbers, analytic functions, power series, Cauchy's theorem and residue theorem.

Prerequisites:

MATH 223 and MATH 290 with “C” (2.0) or better or consent

The topology of Euclidean spaces, compact and connectedness, properties of continuous functions, differentiation, basic theory of Riemann-Stieltjes integration and the fundamental theorem of Calculus.

Prerequisites:

MATH 223 and MATH 290 with “C” (2.0) or better or consent 

A continuation of Math 417. The course may include topics from metric spaces, Riemann-Stieltjes integration, differentiation in Euclidean space, sequences and series of functions, approximation theorems, implicit and inverse function theorems, equicontinuity, and mapping theorems.

Prerequisites:

MATH 417 with “C” (2.0) or better or consent

This course presents the theory, computations, and applications of partial differential equations and Fourier series.

Prerequisites:

MATH 223 and MATH 321 with “C” (2.0) or better or consent

This course presents topics from mathematical analysis of both discrete and continuous models taken from problems in the natural sciences, economics and resource management.

Prerequisites:

MATH 223 and MATH 247 with “C” (2.0) or better or consent

Simplex method and its variants, duality, sensitivity analysis, interior-point methods, quadratic programming and linear complementarity problems. Applications such as classification problems and game theory with linear optimization software.

Prerequisites:

MATH 122, MATH 247

Geometry of spaces including Euclidean and non-Euclidean and applications of contemporary geometry.

Prerequisites:

MATH 247 and MATH 290 with grade of "C" (2.0) or higher or consent.

Euclidean algorithm, primes, composites, number theoretic functions, congruencies, Diophantine equations, Euler and Fermat theorems, algebraic number fields.

Prerequisites:

MATH 345 with “C” (2.0) or better or consent

A continuation of MATH 345. The course will include topics from groups, rings, and fields.

Prerequisites:

MATH 345 with “C” (2.0) or better or consent

An in-depth study of linear operators and their related spaces, dimension, rank, matrix representation of linear operators, special matrices, determinants, eigenvectors and eigenvalues.

Prerequisites:

MATH 345 with “C” (2.0) or better or consent

A mathematical approach to statistics with derivation of theoretical results and of basic techniques used in applications. Includes probability, continuous probability distributions, multivariate distributions, functions of random variables, central limit theorem and statistical inference. Same as STAT 455.

Prerequisites:

MATH 223 with “C” (2.0) or better or consent

A mathematical approach to statistics with derivation of theoretical results and of basic techniques used in applications, including sufficient statistics, additional statistical inference, theory of statistical tests, inferences about normal models and nonparametric methods. Same as STAT 456.

Prerequisites:

MATH 455 / STAT 455 with “C” (2.0) or better or consent

This course applies probabilistic methods to problems encountered in actuarial science that prepares students for the Society of Actuaries Exam P/1.

Prerequisites:

(MATH 354, STATS 354, MATH 455 or STAT 455) and MATH 223