|
|
| YZM1002 | Linear Algebra | 3+0+0 | ECTS:5 | | Year / Semester | Spring Semester | | Level of Course | First Cycle | | Status | Compulsory | | Department | DEPARTMENT of SOFTWARE ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Doç. Dr. Esma ULUTAŞ | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | To give general information to students on the basics of mathematical approach and linear algebra. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | Get used to vector,, matrix, system of linear equations notations | 1,2 | 1, | | LO - 2 : | will understand elementary row operations | 1,2 | 1, | | LO - 3 : | Recognize the concept of determinant | 1,2 | 1, | | LO - 4 : | will be able to explain the concepts of eigenvalues and eigenvectors and diagonalize matrices. | 1,2 | 1, | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome | | |
| Matrices and Matrix Algebra, Elemantery row operations, Systems of Linear Equations, Gaussian Elimination and Gauss-Jordan Methods, Inverse Matrices, Determinants, Minors and Cofactors, Cramer's Rule, Vectors, Scalar, Vector and Mix Product, Vector Spaces, Linear Independent Vectors, Rank of a Matrix, Eigenvalues and Eigenvectors, Base, Orthogonal and Orthonormal Bases, Gram-Schmidt Orthogonalization Method , Diagonalization of a Matrix.
|
| |
| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Matrices and Matrix Algebra | | | Week 2 | Elemantery row operations | | | Week 3 | Systems of Linear Equations | | | Week 4 | Gaussian Elimination and Gauss-Jordan Methods | | | Week 5 | Inverse Matrices | | | Week 6 | Determinants | | | Week 7 | Minors and Cofactors, Cramer's Rule | | | Week 8 | Vectors | | | Week 9 | Mid-term Exam | | | Week 10 | Scalar, Vector and Mix Product | | | Week 11 | Vector Spaces, Linear Independent Vectors, Rank of a Matrix | | | Week 12 | Eigenvalues and Eigenvectors | | | Week 13 | Base, Orthogonal and Orthonormal Bases | | | Week 14 | Gram-Schmidt Orthogonalization Method | | | Week 15 | Diagonalization of a Matrix. | | | Week 16 | Final Exam | | | |
| 1 | Seymour Lipschutz, Ph.D., Marc Lipson, Ph.D.; Lineer Cebir, Nobel Akademik Yayıncılık, Ankara, 2013 | | | |
| 1 | Hacısalihoğlu, H. H., 1982; Lineer Cebir, Bizim Büro, Ankara | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | 10.04.2015 | 1 | 50 | | End-of-term exam | 16 | 02.06.2015 | 1 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Sınıf dışı çalışma | 6 | 14 | 84 | | Arasınav için hazırlık | 2 | 5 | 10 | | Arasınav | 1 | 1 | 1 | | Dönem sonu sınavı için hazırlık | 12 | 1 | 12 | | Dönem sonu sınavı | 1 | 1 | 1 | | Total work load | | | 150 |
|