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**

**Email: **

**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 17 ^{th}, March 31^{st}
and April 28^{th} .^{ }**

**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
5 ^{th} May, from 8:00 to 10:00. **

**W
Deadline: March 3 ^{rd} 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.