Linear Algebra I

The course is an introductory course in linear algebra. It covers the study of systems of linear equations, matrices, determinants, vector spaces, subspaces, basis of vector spaces and subspaces, eigenvalues, eigenvectors, and diagonalization. Computer explorations using MATLAB are an integral component of the course. Prerequisite: Sophomore Standing. Topics include: surfaces, functions of two or more variables, partial derivatives, Lagrange multipliers, double integrals using Cartesian and polar coordinates, triple integrals using Cartesian, cylindrical, and spherical coordinates, integration of vector fields, and the Green’s Theorem. Prerequisite: MAT 213. MAT 226 Probability and Statistics for Computer Science (3.0 cr.); 3 cr. The course is designed to introduce computer science students to the essential probability and statistics concepts needed in their field. The material covers fundamental statistical concepts, including measures of location and variability, graphical techniques, sampling methods, and data interpretation using models. It also covers concepts about basic probability, discrete and continuous random variables, and their respective distributions. Students will also learn about key sampling distributions, the central limit theorem, and the construction of confidence intervals and hypothesis testing using t-distribution and chi-square distribution. Practical applications will be demonstrated using R/RStudio software to illustrate these concepts. Prerequisite: MAT 213.