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ITU-TRNC Student Affairs ITU-TRNC Student Affairs ITU-TRNC Student Affairs ITU-TRNC Student Affairs
  • COURSES
    • COURSE PLANS
    • COURSE SCHEDULES
    • COURSE CATALOGUES
  • FORMS & PETITIONS
    • FORMS
    • PETITIONS
  • INTERNSHIP
    • INTERNSHIP INFORMATION
    • GENERAL INTERNSHIP PRINCIPLES
    • INTERNSHIP PROCESS
    • INTERNSHIP DOCUMENTS
    • ADDITIONAL INTERNSHIP PRINCIPLES
    • PROGRAM INTERNSHIP COMMISSION COORDINATORS
  • TRANSFER
    • ACADEMIC TRANSFER
    • DUAL PROGRAMS
    • EQUIVALENCE DOCUMENT
  • TUITION
    • FEES
    • SCHOLARSHIPS
  • REGULATIONS
    • RULES AND REGULATIONS
    • REGISTRATION PROCEDURES AND PRINCIPLES
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MTH 204

DERS PROGRAMI FORMU

COURSE SYLLABUS FORM

Dersin Adı Course Name
Lineer Cebir Linear Algebra
Kodu
(Code)
Yarıyılı
(Semester)
Kredi
(Credit)
AKTS Kredisi
(ECTS Credit)
Ders Uygulaması, Saat/Hafta
(Course Implementation, Hours/Week)
Ders
(Theoretical)
Uygulama
(Tutorial)
Laboratuvar
(Laboratory)
MTH 204 2 3 5 3 - -
Bölüm/Program
(Department/Program)
Mimarlık
(Architecture)
Dersin Türü
(Course Type)
Zorunlu (Compulsory) Dersin Dili
(Course Language)
İngilizce (English)
Dersin Önkoşulları
(Course Prerequisites)
Yok
(None)
Dersin Mesleki Bileşene Katkısı, %
(Course Category by Content, %)
Temel Bilim ve Matematik
(Basic Sciencesand Math)
Temel Mühendislik
(Engineering Science)
Mühendislik/Mimarlık Tasarım
(Engineering/Architecture Design)
Genel Eğitim
(General Education)
100 - - -
Dersin Tanımı
(Course Description)
Matrisler ve denklem sistemleri. Lineer denklem sistemleri. Satır basamak form. Matris cebri. Elemanter matrisler. Determinantlar. Bir matrisin determinantı. Determinantın özellikleri. Cramer Kuralı. Vektör uzayları. Vektör uzayının tanımı. Alt uzaylar. Lineer bağımsızlık. Baz ve boyut. Bazların değişimi. Satır uzayı ve sütun uzayı. Lineer dönüşümler. Lineer dönüşümün matris temsili. Ortogonallik. Skaler çarpım. Ortogonal alt uzaylar. İç çarpım uzayları. Ortonormal kümeler. Gram-Schmidt yöntemi. Özdeğerler ve özvektörler. Köşegenleştirme.
Matrices and system of equations. Systems of linear equations. Row echelon form. Matrix algebra. Elementary matrices. Determinants. The determinant of a matrix. Properties of determinants. Cramer’s Rule. Vector spaces. Definition of vector space. Subspaces. Linear independence. Basis and dimension. Change of basis. Row space end column space. Linear transformations. Matrix representations of linear transformations. Orthogonality. The scalar product. Orthogonal subspaces. Inner product spaces. Orthonormal sets. The Gram-Schmidt orthogonalization process. Eigenvalues and eigenvector. Diagonalization.
Dersin Amacı
(Course Objectives)
  1. Lineer denklem sistemlerinin çözüm yöntemlerini öğretmek.
  2. Matris ve determinant kavramlarını uygulamada kullanma becerisi sağlamak.
  3. Lineer cebir bilgisini mühendislik problemlerini çözmede kullanabilme becerisi kazandırmak.
  1. To provide the methods of solution of systems of linear equations.
  2. To provide the applications of matrix and determinant.
  3. To give an ability to apply knowledge of linear algebra on engineering problems.
Dersin Öğrenme Çıktıları
(Course Learning Outcomes)

Bu dersi başarı ile tamamlayan öğrenciler;

  1. Lineer denklem sistemlerinin çözümünü bulabilir, matrislerle aritmatik işlemler yapabilir ve matrisin tersini bulabilir.
  2. Determinantı hesaplayabilir ve Cramer Kuralını kullanarak lineer sistemleri çözebilir.
  3. Vektör uzayları, baz ve boyut kavramlarını öğrenir.
  4. Lineer dönüşümün matris ile temsil edilebileceğini görür.
  5. Gram-Schmidt yöntemi ile bir bazı ortonormal baza çevirebilir.
  6. Matrislerin özdeğerlerini ve özvektörlerini bulabilir.

Students who pass the course will be able to;

  1. Solve the systems of linear equations, provide arithmetic operations with matrices and compute the inverse of matrix.
  2. Determine the value of determinant of a matrix and use Cramer Rule to solve the systems.
  3. Learn the importance of the concepts of vector space, basis and dimension.
  4. Compute the matrix representation of a linear transformation.
  5. Find an orthonormal basis using the Gram-Schmidt process.
  6. Evaluate the eigenvalues and the corresponding eigenvectors of the matrix.
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  • Namık Kemal Mah.
  • Fazıl Polat Paşa Bulvarı
  • Famagusta
  • Turkish Republic of Northern Cyprus
  • 99450
  • (+90) 392 630 5000
  • kktcogrenciisleri@itu.edu.tr
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