38 courses with the subject MATH, 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.
two years of high school mathematics preparation or who are otherwise deficient in mathematics. A basic algebra course to prepare students for MATH 101. Note: Does not earn College credit (offered when need arises).
Mathematical skills for students with fewer than two years of high school mathematics preparation or who are otherwise deficient in mathematics. A basic algebra course to prepare students for MATH 101.
An introductory course in algebra, intended to prepare students for calculus, precalculus, and higher-level studies in mathematics. Emphasis will be on solving problems symbolically, numerically, and graphically, as well as interpreting and analyzing solutions to understand the connections among these methods. Includes a one hour recitation per week. Students should enroll in this course only if they also intend to take one or more of Math 104, Math 109, or Math 201. Students who have earned a “C” or better in a math course for which degree credit is awarded may not register for or earn credit for Math 101. Reserved or restricted to: STEM Majors, Education Majors, and Finance Majors only, or by permission of instructor and Dean. Foundation Category: Quantitative Literacy
Exponents and radicals, quadratic equations, logarithms, and introductory trigonometry.
Preparation for calculus. Curve tracing; algebraic, trigonometric, exponential, and logarithmic functions and their inverses; elements of analytic geometry. Includes a one-hour recitation per week. (Not open to students who have completed MATH 201 or equivalent.) Foundation Category: Quantitative Literacy
210
Mathematical background for modern business methods. Topics in both theory and application; sets, relations; linear and quadratic functions; equations, inequalities; matrices, determinants, linear programming; fundamental analytical geometry; permutations, combinations, probability. Foundation Category: Quantitative Literacy
Selected topics in calculus pertinent to the study of life sciences and managerial and social sciences. Functions, limits, differentiation, integration, methods and applications of the differential and integral calculus. Three lecture hours and one recitation hour per week.
A first course in statistics. Conceptually covers the basics in descriptive and inferential statistics and computational facility with applied statistics; the proper use and interpretation of statistical results. (Not open to business administration, psychology, or special education majors.) Foundation Category: Quantitative Literacy
Introduction to elementary yet rigorous mathematics from several different branches of mathematics that play a role in the enciphering and deciphering of messages: number theory, abstract algebra, matrix algebra, probability, and statistics. Prerequisites: Honors Program students only; MATH 101 or above. Foundation Category: Quantitative Literacy
Introduction to problem solving techniques, the basic of sets, the algorithms of arithmetic and mental techniques for arithmetic, the rules for divisibility, and the calculation and use associated with divisors, the arithmetic of integers and models used to teach them, the basics of working with and using fractions, decimals and percentages, and algebraic and graphical reasoning to solve problems. Prerequisite: open to majors in the School of Education. Foundation Category:
The real number system; inequalities, absolute value, analytic geometry; functions; limits; derivatives and their applications. Prerequisite: Minimum grade of C- in MATH 104 or equivalent. Foundation Category: Quantitative Literacy.
The definite integral; trigonometric and exponential integration; integration by parts, partial fractions and trigonometric substitutions; applications; improper integrals; vectors.
Selected topics from antiquity to present times. Contributions of different cultures to the field of mathematics will be discussed. Prerequisite: Pass MATH 104 or equivalent, or MATH 201 or equivalent. Breadth & Proficiency: Global Cultures & Languages.
This course is a first course in mathematical thinking. It is intended as an introduction to mathematical proof, and students who finish the course should achieve maturity in mathematical communication. This course covers an introduction to set theory, methods of proof, analysis of proof, induction, logical reasoning, relations, and basic number theory.
Offered occasionally. Topics vary with each offering.
Sequences, Taylor’s series, infinite series; partial differentiation; cylindrical and spherical coordinates; multiple integration and applications, vector algebra, gradients.
Linear equations and matrices, vector spaces, subspaces, linear independence, bases, dimension, determinants, linear transformations, eigenvectors, diagonalization, and orthogonality. Prerequisite: “C-” grade or better in MATH 202.
First and second order differential equations; techniques for solution and application, series solution; Laplace transforms. Prerequisite: MATH 301.
Combinatorics; probability models, conditional probability and independence; discrete and continuous random variables; distribution functions and densities; moments; characteristic and moment generating functions; limit theorems. 212
The Gamma function; commonly used distributions and densities, point estimation, hypothesis testing, confidence intervals and analysis of variance. Prerequisite: MATH 304.
Line intervals, vector calculus, Fourier series, Fourier transforms, Laplace transforms, partial differential equations and applications in engineering. Prerequisite: MATH 303.
Problem solving techniques, number systems—including past systems, non-decimal systems, and the decimal number system—and arithmetic in such, the basics of geometric reasoning and problem solving, techniques and uses for measurements in geometry, transformations, symmetries, tilings, congruence and similarity of figures, introductory statistics and statistical inference, and introductory probability. This class may not be used in any major to satisfy a “MATH 300 or above” requirement.
mathematics majors.)
This course will cover the order of complexity, recursion, recurrence relations, graph theory, probability, statistics, and matrices. Emphasis is placed on providing a context for the application of mathematics within computer science. Prerequisite:
In this course, students will be introduced to the advanced fields of mathematics used within data science and machine learning. The major topics will include multivariate calculus (partial derivatives, gradients and Jacobian matrices, etc.), linear algebra (vector spaces, matrices, linear transformations, eigenvalues & vectors, etc.), optimization, and advanced topics from probability theory and statistics (commonly used distributions and densities (e.g. multivariate Gaussians), hypothesis testing, confidence intervals and analysis of variance). With these mathematical tools in hand, students will be well equipped to explore the theory and algorithms of machine learning. Prerequisite: MATH 304 213
This course will teach students the algorithms and mathematics underlying the most popular machine learning models. Students will study machine learning within the broad framework of optimization and loss minimization using techniques such as maximum likelihood estimation and gradient descent via backpropagation. The major theoretical machine learning concepts will be introduced, such as model fit and regularization, generalization, and bias-variance tradeoff. The ML models studied will include k-nearest neighbors, decision trees and forests, linear regression and logistic regression, hard- and soft-margin support vector machines, and neural networks. Students will use Python to implement basic versions of ML models and optimization algorithms by hand. As time permits, additional topics may include ensemble methods, bagging and boosting, dimensionality reduction, the use of kernels, and reinforcement learning. Prerequisite:Students are expected to have a strong grasp of Python programming and Students must have received a C- or better in both MATH/DS 312: Math for Data Science and CS 301: Artificial Intelligence (Also offered as CS315/DS315)
Compound interest, accumulated values; nominal and effective interest rates; annuities; present values; amortization; bonds. Prerequisite: Pass MATH 104, 108, 109, 201 or equivalent.
Probability, mortality tables, single life functions; net; premiums for life annuities and insurance benefits; reserves. Prerequisite: MATH 350.
Computer arithmetic, solutions of nonlinear equations; solving systems of linear equations, splines; numerical differentiation and integration; numerical solutions of differential equations Prerequisites: C- or better in MATH 109 or MATH 201 (MATH 301 and MATH 302 are strongly suggested, but not required)
(Also offered as BUSA 381. See BUSA 381 for course description.)
Groups, subgroups, permutations, cyclic groups, isomorphisms, homomorphisms, rings, integral domains, fields. Prerequisite: “C-” grade or better in MATH 202.
Divisibility; distribution of primes; congruences; number-theoretic functions; primitive roots and indices; quadratic reciprocity; sums of squares. Prerequisite: “C-” grade or better in MATH 202. 214
Selected topics from Euclidean and non-Euclidean geometries. Further topics in higher geometry as time permits. Prerequisite: MATH 302, 308, or 390 (or permission of instructor)
The real number system; sequences; limits and continuity; differential calculus; Riemann integrals; infinite series; sequences of functions. Prerequisite: MATH 301.
Complex numbers; functions of a complex variable; limits and continuity; analytic functions; complex integration; sequences and series; residue theory; conformal mappings. Prerequisite: MATH 301.
This course covers the theoretical background of the Bayesian approach to data science (including the choice of prior distributions and calculation of posterior distributions) with an emphasis on practical applications to inference problems. Topics covered include Bayes’ Theorem, prior distributions, inferences for discrete random variables, inferences for continuous random variables, linear regression, analysis of variance, MCMC/Gibbs sampler, and hierarchical models. Prerequisite: MATH 304
Source: St. Thomas Aquinas College's catalog, linked per course · table learning_unit · CourseShelf publish 61
