|
| Course Description |
|
| Course Name |
: |
Linear Algebra I |
|
| Course Code |
: |
ENM135 |
|
| Course Type |
: |
Compulsory |
|
| Level of Course |
: |
First Cycle |
|
| Year of Study |
: |
1 |
|
| Course Semester |
: |
Fall (16 Weeks) |
|
| ECTS |
: |
4 |
|
| Name of Lecturer(s) |
: |
|
|
| Learning Outcomes of the Course |
: |
Grasps matrix and vector algebra.
Calculates determinants and eigenvalues
Solves linear systems of equations
Analyzes vector functions.
Calculates gradient, directional derivative, curl and divergence.
|
|
| Mode of Delivery |
: |
Face-to-Face |
|
| Prerequisites and Co-Prerequisites |
: |
None |
|
| Recommended Optional Programme Components |
: |
None |
|
| Aim(s) of Course |
: |
To teach the student matrix and vector algebra, linear independence and solution of linear systems of equations.
|
|
| Course Contents |
: |
Introduction to matrices, matrix algebra. Solution of linear systems of equations with Gaussian elimination. Vector spaces, linear independence, rank of a matrix. Fundamental theorem of linear equations, finding inverse with Gauss-Jordan elimination. Determinants, Cramer´s rule. Eigenvalues and eigenvectors, similar matrices, similarity transformations. Vectors and vector algbera. Vector analytical geometry, line and plane equations. Vector functions. Gradient, directional derivative, divergence and curl. |
|
| Language of Instruction |
: |
Turkish |
|
| Work Place |
: |
IE classrooms |
|
|
Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
|
1 |
Introduction to matrices and linear systems of equations |
|
lecture, discussion |
|
2 |
Matrix algebra, transpose and inverse of a matrix |
|
lecture, discussion |
|
3 |
Solution of linear systems with Gaussian elimination |
|
lecture, discussion |
|
4 |
Linear independence, rank of a mtarix |
|
lecture, discussion |
|
5 |
Fundamental theorem of linear systems, finding inverse through Gauss-Jordan elimination |
|
lecture, discussion |
|
6 |
Determinants, Cramer´s Rule |
|
lecture, discussion |
|
7 |
Eigenvalues |
|
lecture, discussion |
|
8 |
Review, midterm exam |
|
review, written exam |
|
9 |
Similar matrices and diagonalization |
|
lecture, discussion |
|
10 |
Introduction to vectors, vector algebra, vector products |
|
lecture, discussion |
|
11 |
Analytical geometry with vectors, line and plane equations |
|
lecture, discussion |
|
12 |
Vector functions and their analysis |
|
lecture, discussion |
|
13 |
Gradient and directional derivative |
|
lecture, discussion |
|
14 |
Curl and divergence |
|
lecture, discussion |
|
15 |
Review |
|
review |
|
16/17 |
Final Exam |
|
written exam |
|
|
|
Required Course Resources |
| Resource Type | Resource Name |
| Recommended Course Material(s) |
 Elemantary Linear Algebra - Keith Matthews
|
| |
| Required Course Material(s) | |
|
|
|
Assessment Methods and Assessment Criteria |
|
Semester/Year Assessments |
Number |
Contribution Percentage |
|
Mid-term Exams (Written, Oral, etc.) |
1 |
90 |
|
Homeworks/Projects/Others |
1 |
10 |
|
Total |
100 |
|
Rate of Semester/Year Assessments to Success |
40 |
| |
|
Final Assessments
|
100 |
|
Rate of Final Assessments to Success
|
60 |
|
Total |
100 |
|
|
| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
|
1 |
Can collect and analyze data required for industrial engineering problems ,develops and evaluates alternative solutions. |
2 |
|
2 |
Has sufficient background on topics related to mathematics, physical sciences and industrial engineering. |
5 |
|
3 |
Gains ability to use the acquired theoretical knowledge on basic sciences and industrial engineering for describing, formulating and solving an industrial engineering problem, and to choose appropriate analytical and modeling methods. |
4 |
|
4 |
Gains ability to analyze a service and/or manufacturing system or a process and describes, formulates and solves its problems . |
2 |
|
5 |
Gains ability to choose and apply methods and tools for industrial engineering applications. |
3 |
|
6 |
Can access information and to search/use databases and other sources for information gathering. |
1 |
|
7 |
Works efficiently and takes responsibility both individually and as a member of a multi-disciplinary team. |
1 |
|
8 |
Appreciates life time learning; follows scientific and technological developments and renews himself/herself continuously. |
1 |
|
9 |
Can use computer software in industrial engineering along with information and communication technologies. |
3 |
|
10 |
Can use oral and written communication efficiently. |
2 |
|
11 |
Has a conscious understanding of professional and ethical responsibilities. |
1 |
|
12 |
Uses English skills to follow developments in industrial engineering and to communicate with people in his/her profession. |
1 |
|
13 |
Has a necessary consciousness on issues related to job safety and health, legal aspects of environment and engineering practice. |
1 |
|
14 |
Becomes competent on matters related to project management, entrepreneurship, innovation and has knowledge about current matters in industrial engineering. |
1 |
| * Contribution levels are between 0 (not) and 5 (maximum). |
|
|
| Student Workload - ECTS |
| Works | Number | Time (Hour) | Total Workload (Hour) |
| Course Related Works |
|
Class Time (Exam weeks are excluded) |
14 |
3 |
42 |
|
Out of Class Study (Preliminary Work, Practice) |
14 |
3 |
42 |
| Assesment Related Works |
|
Homeworks, Projects, Others |
1 |
5 |
5 |
|
Mid-term Exams (Written, Oral, etc.) |
1 |
10 |
10 |
|
Final Exam |
1 |
10 |
10 |
|
Total Workload: | 109 |
| Total Workload / 25 (h): | 4.36 |
| ECTS Credit: | 4 |
|
|
|