# GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
LINEAR ALGEBRA/MAT 104
 Course Title: LINEAR ALGEBRA Credits 3 ECTS 5 Semester 2 Compulsory/Elective Compulsory
COURSE INFO
-- LANGUAGE OF INSTRUCTION
Turkish
-- NAME OF LECTURER(S)
Assoc.Prof. Ercan ALTINIŞIK; Assoc.Prof. Naim TUĞLU
-- WEB SITE(S) OF LECTURER(S)
http://websitem.gazi.edu.tr/site/ealtinisik , http://websitem.gazi.edu.tr/site/naimtuglu
-- EMAIL(S) OF LECTURER(S)
ealtinisik@gazi.edu.tr , naimtuglu@gazi.edu.tr
-- LEARNING OUTCOMES OF THE COURSE UNIT
understand fundamental concepts of matrix algebra, perform matrix operations
solve systems of linear equations using matrices
understand fundamental concepts of vector spaces and subspaces
explain the concepts of basis and dimension of a vector space
write a vector with respect to different basis of the vector space and find the coordinates of the vector wrt a given basis
calculate the norm of a vector in an inner vector space and determine two vectors is whether orthogonal or not
orthogonalize given n linearly independet vectors using the Gram–Schmidt process
solve various problems using linear transformations
find the image and the null space of a linear transformation and determine dimensions of them
find eigenvalues and eigenvectors of a matrix or a linear transformaiton and diagonalize them
-- MODE OF DELIVERY
The mode of delivery of this course is Face to face
-- PREREQUISITES AND CO-REQUISITES
There is no prerequisite or co-requisite for this course.
-- RECOMMENDED OPTIONAL PROGRAMME COMPONENTS
There is no recommended optional programme component for this course.
 --COURSE CONTENT 1. Week Matrices and matrix operations 2. Week Elementary row operations and column operations and their applicaitons 3. Week Determinants and their applications 4. Week Systems of linear equations and solution methods 5. Week Definitions of vector spaces and subspaces 6. Week Linear dependence and independence 7. Week Basis and dimensions, coordinates of a vector wrt a basis 8. Week Midterm exam 9. Week Inner pruduct and norm 10. Week Orthogonal vectors and Gram-Schmidt orthogonaliztion process 11. Week The least squares method 12. Week Linear trasformations, the image and the null space of a linear transformation 13. Week Matrix representations of linear transformations, composition of two transformations and inverse of a transformation 14. Week Eigenvalues and eigenvectors 15. Week Diagonalization and its applicaitons 16. Week Final exam