NAME OF COURSE/MODULE: LINEAR ALGEBRA
COURSE CODE: SMG 2023
NAME(S) OF ACADEMIC STAFF: DR. YUMN SUHAYLA BT
RATIONALE FOR THE INCLUSION OF THE COURSE/MODULE IN THE PROGRAMME: This course provides the basic concepts of linear algebra which is needed for the higher level courses of the program.
SEMESTER AND YEAR OFFERED: SEM 1 / YEAR II
TOTAL STUDENT LEARNING TIME (SLT) FACE TO FACE TOTAL GUIDED AND INDEPENDENT LEARNING
L = Lecture

T = Tutorial

P = Practical

O= Others

L

28

T

 

14

P

 

0

O

 

78

L + T + P + O = 120 HOURS

CREDIT VALUE: 3
PREREQUISITE (IF ANY): NONE
OBJECTIVES: 1.       To provide students the method of solving system of linear equations.

2.       To determine the subspaces that may exists from a matrix.

3.       To construct the orthonormal basis and hence determine the diagonal matrix.

4.       To find a linear transformation between two spaces.

LEARNING OUTCOMES:

 

Upon successful completion of this course students should have the ability to:

1.        Solve any system of linear equations the related to the topics discussed.( LO1 – C4)

2.        Find the row-space, column-space and null-space of a matrix. (LO2 – P4)

3.        Use the Gaussian and Gauss-Jordan elimination to solve selected system oflinear equations as demonstrated by theinstructor. (LO3 – CTPS5)

TRANSFERABLE SKILLS: Students should be able to develop problem solving skills through a process of lectures and tutorials.
TEACHING-LEARNING AND ASSESSMENT STRATEGY: Teaching-learning strategy:

  • The course will be taught through a combination of formal lectures, and tutorials.

Assessment strategy:

  • Formative
  • Summative
SYNOPSIS:

 

This course begins with solving the system of linear equations in m equations and n unknowns by Gauss-Jordan method followed by the discussion of the ten axioms of vector space. Various types of subspaces such as row-space, column-space, null-space of a matrix are then discussed followed by the discussion of inner product and orthonormal basis. The concept of eigenvalues and eigenvectors which lead to the determination of diagonalization of a matrix is discussed before the last topic about the linear transformations.
MODE OF DELIVERY: Lecture, discussion in tutorial class.
ASSESSMENT METHODS AND TYPES:
A. Continuous Assessment (50%)
Category Percentage
·    Assignments/ Quizzes

·    Test One

·    Test Two

10 %

15 %

25 %

B. Final Examination (50%)
Examination 40 % Essay type questions.
MAIN REFERENCES SUPPORTING THE COURSE 1.         Bernard Kolman, David R. Hill, (2008). Elementary Linear Algebra With Applications, 9th Edition. Pearson. Prentice Hall.
ADDITIONAL REFERENCES SUPPORTING THE COURSE 1.          L.E. Spence, A.J. Insel, S.H. Friedberg, (2008). Elementary Linear Algebra, A matrix approach, Second Edition, Pearson. Prentice Hall.

2.          David C. Lay, (2006). Linear Algebra and Its Applications. Pearson Addision-Wesley Publishing Co. Inc.

3.          Steven J. Leon, (2006). Linear Algebra with Applications. 7th Edition. Pearson. Prentice Hall International.

4.          Howard Anton, Chris Rorres, (2005). Elementary Linear Algebra. 9th Edition. John Wiley & Sons.