MATH 2853 – Linear Algebra-Spring 2006


Instructor:      Dr. A. Boumenir

Location:        Monday, Wednesday, Friday, 10:00-10-50 in Boyd 302.

Office Hours: Monday, Wednesday and Thursday 12:00-14:50 or by appointment.

Office:             Boyd 321

Phone: 678-839-4131


Textbook:       Introductory Linear Algebra: An Applied First Course,

by Kolman and Hill. 8/E Prentice Hall.


Course Objective: Methods of solving systems of linear equations (Gaussian elimination, Gauss-Jordan elimination, Cramer’s Rule, LU-decomposition), vectors (dot product, projection, linear independence, span, basis and dimension), matrices (properties of matrices, matrix algebra, determinants, eigenvalues and eigenvectors), linear transformations, and applications.

Computer labs:  Will take place in TLC, on Fridays (TBA), and will use Maple V10.


Prerequisite: MATH 2644
Tests:              There will be three in class tests, 100 points each, given on Friday:

                        February 17th, March 31st and April 28th .

Quizzes:          A weekly quiz/hw will be given on Fridays and will mainly cover questions from the homework.  Each quiz counts 20 points and the best 10 are counted towards your final grade.

Final:               Friday 5th May, from 8:00 to 10:00.             

W Deadline:  March 3rd is the last day to withdraw with grade of W.

Attendance:    8 absences lead to WF.

Evaluation:     Tests= 300 points, Quizzes/Hw= 200 points, Final= 200 points,

Grading:         700-630: A,     629-560: B,     559-490: C,     489-420: D, Below 420: F.


Learning Outcomes: The student will have an understanding of:

1. How to perform basic vector operations.

2. How to compute the inverse or determinant of a square matrix.

3. How to compute the LU-decomposition of a square matrix.

4. How to express linear systems of equations in matrix form.

5. How to solve systems of linear equations using  Gauss-Jordan elimination.

6. How to solve systems of linear equations using Cramer’s Rule.

7. The geometric properties of vectors.

8. The basic properties of real vector spaces and subspaces including properties such as linear independence, span, basis, rank.

9. How to analyze linear transformations.

10. How to diagonalize a square matrix and find its eigenvalues and eigenvectors.

11. Use MAPLE to perform basic matrix operations.