COURSE DESCRIPTION

This course covers matrix theory and linear algebra, emphasizing topics useful in other disciplines. Linear algebra is a branch of mathematics that studies systems of linear equations and the properties of matrices. The concepts of linear algebra are extremely useful in physics, economics and social sciences, natural sciences, and engineering. One major contribution to the topic was made by Gauss (1777–1855), who was confronted with large systems of linear equations in his work on astronomy and developed the famous method of least squares to cope with measurement errors. Later in the nineteenth century Cauchy, Sylvester, Cayley and others developed the concept of a matrix, which provides the most convenient language for the theory and practice of linear equations. Matrices are intricate algebraic objects with many fascinating properties, but they also provide a bridge between linear equations and vectors, so infusing the subject of linear algebra with a strong geometric flavor. We will delve into all these topics, as well as the notions of determinant and eigenvalues, which are important numbers associated with any square matrix.

2. AIMS & OBJECTIVES (General and Specific Course Objectives:)

  • To provide students with a good understanding of the concepts and methods of linear algebra, described in detail in the syllabus.
  • To help the students develop the ability to solve problems using linear algebra.
  • To connect linear algebra to other fields both within and without mathematics.
  • To develop abstract and critical reasoning by studying logical proofs and the axiomatic method as applied to linear algebra.

3. BY THE END OF THIS COURSE STUDENT WILL BE ABLE TO

  1. Solving linear systems of equations with Gauss Jordan methods
  2. Fundamental properties of matrices and matrix algebra including inverse matrices
  3. Determinants and their properties
  4. Cramer's Rule to solve system of linear equations
  5. Eigenvalues and eigenvectors
  6. Vector spaces (subspaces).
  7. Orthogonal bases and orthogonal projections