Faculty Of Engıneerıng
Electrıcal And Electronıcs Engıneerıng (Englısh)
Course Information
| LINEAR ALGEBRA |
| Code |
Semester |
Theoretical |
Practice |
National Credit |
ECTS Credit |
| Hour / Week |
| MAT201 |
Fall |
3 |
0 |
3 |
4 |
| Prerequisites and co-requisites |
|
| Language of instruction |
English |
| Type |
Required |
| Level of Course |
Bachelor's |
| Lecturer |
Asst. Prof. Ali Kemal HAVARE |
| Mode of Delivery |
Face to Face |
| Suggested Subject |
|
| Professional practise ( internship ) |
None |
| Objectives of the Course |
An exposure to linear systems and linear relationships. Using matrices to represent linear systems, and vector spaces.
|
| Contents of the Course |
Systems of linear equations. Matrices, matrix algebra determinants. Vector spaces, subspaces, orthogonal spaces. Charactersitic equation of matrix, eigenvalues, eigenvectors. Cayley-Hamilton Theorem.
|
Learning Outcomes of Course
| # |
Learning Outcomes |
| 1 |
Getting knowledge about Linear Eqwuations and matrices
|
| 2 |
Getting knowledge about Determinants
|
| 3 |
Getting knowledge about Solving linear systems
|
| 4 |
Getting knowledge about Real vector spaces |
| 5 |
Getting knowledge about Eigenvalues and eigenvectors
|
Course Syllabus
| # |
Subjects |
Teaching Methods and Technics |
| 1 |
Linear Eqwuations and matrices
|
lecture |
| 2 |
Solving linear systems
|
lecture |
| 3 |
Solving linear systems
|
lecture |
| 4 |
Determinants
|
lecture |
| 5 |
Determinants
|
lecture |
| 6 |
Real vector spaces
|
lecture |
| 7 |
Real vector spaces
|
lecture |
| 8 |
Real vector spaces
|
lecture |
| 9 |
Real vector spaces
|
lecture |
| 10 |
Midterm
|
|
| 11 |
Inner product spaces
|
lecture |
| 12 |
Inner product spaces
|
lecture |
| 13 |
Eigenvalues and eigenvectors
|
lecture |
| 14 |
Eigenvalues and eigenvectors
|
lecture |
| 15 |
|
|
| 16 |
Final Exam |
|
Course Syllabus
| # |
Material / Resources |
Information About Resources |
Reference / Recommended Resources |
| 1 |
Internet resources |
|
|
| 2 |
B. Kolman, D. Hill, Elementary Linear Algebra with Applications
|
|
|
Method of Assessment
| # |
Weight |
Work Type |
Work Title |
| 1 |
40% |
Mid-Term Exam |
Mid-Term Exam |
| 2 |
60% |
Final Exam |
Final Exam |
Relationship between Learning Outcomes of Course and Program Outcomes
| # |
Learning Outcomes |
Program Outcomes |
Method of Assessment |
| 1 |
Getting knowledge about Linear Eqwuations and matrices
|
1 |
1͵2 |
| 2 |
Getting knowledge about Determinants
|
1 |
1͵2 |
| 3 |
Getting knowledge about Solving linear systems
|
1 |
1͵2 |
| 4 |
Getting knowledge about Real vector spaces |
2 |
1͵2 |
| 5 |
Getting knowledge about Eigenvalues and eigenvectors
|
3 |
1͵2 |
PS. The numbers, which are shown in the column Method of Assessment, presents the methods shown in the previous table, titled as Method of Assessment.
Work Load Details
| # |
Type of Work |
Quantity |
Time (Hour) |
Work Load |
| 1 |
Course Duration |
14 |
3 |
42 |
| 2 |
Course Duration Except Class (Preliminary Study, Enhancement) |
14 |
3 |
42 |
| 3 |
Presentation and Seminar Preparation |
0 |
0 |
0 |
| 4 |
Web Research, Library and Archival Work |
0 |
0 |
0 |
| 5 |
Document/Information Listing |
0 |
0 |
0 |
| 6 |
Workshop |
0 |
0 |
0 |
| 7 |
Preparation for Midterm Exam |
1 |
17 |
17 |
| 8 |
Midterm Exam |
1 |
2 |
2 |
| 9 |
Quiz |
0 |
0 |
0 |
| 10 |
Homework |
0 |
0 |
0 |
| 11 |
Midterm Project |
0 |
0 |
0 |
| 12 |
Midterm Exercise |
0 |
0 |
0 |
| 13 |
Final Project |
0 |
0 |
0 |
| 14 |
Final Exercise |
0 |
0 |
0 |
| 15 |
Preparation for Final Exam |
1 |
25 |
25 |
| 16 |
Final Exam |
1 |
2 |
2 |
| |
130 |
