Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
LINEAR ALGEBRA MAT0120 4. Semester 3 + 0 3.0 5.0

Prerequisites None

Language of Instruction Turkish
Course Level Bachelor's Degree
Course Type Compulsory
Mode of delivery
Course Coordinator
Instructors
Assistants
Goals The aim of this course is to present the fundamentals of linear algebra in the clearest possible way.
Course Content Systems of linear equations, matrices, vectors in 2-space and 3-space, finite dimensional vector spaces, linear transformations, eigenvalues, eigenvectors, diagonalization and applications.
Learning Outcomes 1) Student gets a general perspective about solving systems of linear equations.
2) Student learns matrix arithmetic and learns, compared to manual calculations, how easy the computers make these computations
3) Student learns the notion of vector in 2-space and 3-space, and therefore understands how the general vector space notion is defined
4) Student learns about finite dimensional vector spaces.
5) By learning inner product spaces, student learns how to make geometrical calculations in a real vector space.
6) Student gets a perspective of linear transformation.
7) Student learns about eigenvalues, eigenspaces and therefore learns how to diagonalize a diagonizable square matrix.
8) Student learns about the applications to differential equations and to approximation problems.

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week Systems of linear equations, Gaussian elimination method Lecture; Question Answer; Problem Solving
Colloquium
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)
2. Week Matrices and matrix operations Lecture; Question Answer; Problem Solving
Colloquium
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)
3. Week Determinant, properties of determinant Lecture; Question Answer; Problem Solving
Colloquium
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)
4. Week Vectors in 2-spaces and 3-spaces, dot product Lecture; Question Answer; Problem Solving
Colloquium
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)
5. Week Cross product, lines and planes in 3-space Lecture; Question Answer; Problem Solving
Colloquium
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)
6. Week Vector spaces, subspaces Lecture; Question Answer; Problem Solving
Colloquium
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)
7. Week Linear independence, basis and dimension Lecture; Question Answer; Problem Solving
Colloquium
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)
8. Week Inner Product Space, orthonormal bases, Gram-Schmidt method Lecture; Question Answer; Problem Solving
Colloquium
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)
9. Week Linear tansformations, kernel and range Lecture; Question Answer; Problem Solving
Colloquium
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)
10. Week Matrices of linear Ttansformations, similarity Lecture; Question Answer; Problem Solving
Colloquium
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)
11. Week Eigenvalues and Eigenvectors, Eigenspaces Lecture; Question Answer; Problem Solving
Colloquium
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)
12. Week Diagonalization, orthogonal diagonalization Lecture; Question Answer; Problem Solving
Colloquium
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)
13. Week Application to differential equations Lecture; Question Answer; Problem Solving
Colloquium
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)
14. Week Application to approximation problems Lecture; Question Answer; Problem Solving
Colloquium
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)

Sources Used in This Course
Recommended Sources
Elementary Linear Algebra with Applications, Bernard Kolman, David R. Hill
Uygulamalı Lineer Cebir, Ömer Akın

Relations with Education Attainment Program Course Competencies
Program RequirementsContribution LevelDK1DK2DK3DK4DK5DK6DK7DK8
PY1500000000
PY2500000000
PY3500000000
PY4500000000

*DK = Course's Contrubution.
0 1 2 3 4 5
Level of contribution None Very Low Low Fair High Very High
.

ECTS credits and course workload
Event Quantity Duration (Hour) Total Workload (Hour)
. 14 3
Course Duration (Total weeks*Hours per week) 14 3
Work Hour outside Classroom (Preparation, strengthening) 14 3
Homework 2 5
Midterm Exam 1 2
Final Exam 1 2
1 2
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course
Quick Access Hızlı Erişim Genişlet
Course Information
  • Course Information
  • Weekly Topics (Content)
  • Sources Used in This Course
  • Relations with Education Attainment Program Course Competencies
  • ECTS credits and course workload