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Course Criteria
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3.00 Credits
This course provides some of the content necessary for elementary school teachers. Topics covered include problem solving, intuitive geometry, and measurement. May only be taken by Early Childhood Education, Elementary Education, and Special Education majors. Three semester hours.
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3.00 Credits
This course provides an introductory overview of linear algebra. Topics include vector and matrix algebra, solutions of systems of linear equations, basis and dimension, eigenvalues and eigenvectors, and matrix decompositions. Students will use technology to apply course content to solve problems in business, computing, and the sciences. Prerequisites: CIS 130 and either MATH 123 or MATH 141. Three credit hours.
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3.00 Credits
An introduction to elementary data analysis, descriptive measures, theoretical distributions of random variables, and sampling distributions of statistics leading to statistical inference including estimation of parameters with confidence intervals and tests of hypotheses. (MATH 311 is recommended for mathematics major and regular option computer science majors and is required for dual-degree engineering students).
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3.00 Credits
A continuation of MATH 211 includes comparing parameters of two or more populations, analyses of count data by means of multinomial distributions and contingency tables, discussion of issues of elementary experimental design, simple linear regression and correlation, analysis of variance methods, and additional topics as time allows. Students will make frequent use of a statistical software package. Prerequisite: MATH 211. Three semester hours.
Prerequisite:
MATH 211
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3.00 Credits
This course is an overview of machine learning techniques that use labeled data to train an algorithm to make predictions about unlabeled data. It provides an introduction to both linear regression and to classification techniques including logistic regression, K-nearest neighbors, support vector machines, tree-based methods, and neural networks. Prerequisites: DSCI 230, MATH 208, and MATH 211. Three credit hours.
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3.00 Credits
This course gives an overview of machine learning techniques that are commonly applied to unlabeled data sets. It provides an introduction to the K-means Clustering and Hierarchical Clustering algorithms as well as the use of Principal Component Analysis. Prerequisites: DSCI 230, MATH 208, and MATH 211. Three credit hours.
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4.00 Credits
Vectors in R2 and R3, functions of several variables, partial differentiation, multiple integrals, applications of multivariable calculus, divergence, curl, line and surface integrals, Green's Theorem and Stokes' Theorem. Prerequisite: "C" or better in MATH 142. Four semester hours.
Prerequisite:
MATH 142
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4.00 Credits
Basic theory and solutions of ordinary linear differential equations. Applications in mechanics and vibrations. Power series solutions at ordinary points and at regular singular points. Introduction to Laplace transform methods and systems of ordinary differential equations. Prerequisites: "C" or better in MATH 241. Four semester hours.
Prerequisite:
MATH 241
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1.00 - 3.00 Credits
This elective course allows for a flexible offering of various mathematical and statistical topics which are not a part of the regular course offerings. Specific topics will be announced in advance. The course may be taken for additional credit as the topic changes. To repeat the course to improve a grade the topic, the course number, and the semester hours must be the same. This course will not be offered more than once a semester. Prerequisite: Permission of the instructor. One to three semester hours.
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1.00 Credits
Designed to give students practical experience in tutoring mathematics. Elective credit only. Graded Pass/Fail. One semester hour.
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