Information Systems Engineering
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Ders Genel Tanıtım Bilgileri

Course Code:

FEC207

Ders İsmi:

Linear Algebra

Ders Yarıyılı:

Fall

Ders Kredileri:

Theoretical	Practical	Laboratory	ECTS
4	0	0	7

Language of instruction:

Turkish

Ders Koşulu:

Ders İş Deneyimini Gerektiriyor mu?:

Yes

Type of course:

Required

Course Level:

Bachelor

TR-NQF-HE:6. Master`s Degree

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Assoc. Prof. Dr. MERVE TEMİZER ERSOY

Course Lecturer(s):

Asst. Prof. Dr. MELİSA RAHEBİ
Prof. Dr. İLKE TAŞCIOĞLU

Course Assistants:

Dersin Amaç ve İçeriği

Course Objectives:

To learn the basics of Linear Algebra and to use this knowledge in solving engineering problems.

Course Content:

In this course, the general concepts of linear algebra are examined under the following topics: Systems of linear equations and matrices, Gaussian elimination method, matrix algebra, inverse of a matrix, elementary matrices, LU decomposition, determinant of a square matrix, properties of determinant, Cramer's rule, vector spaces, sub-spaces. spaces, linear independence, basis and dimension, base change, inner product spaces, orthonormal basis, linear transformations, matrix representation of linear transformation, eigenvalues and eigenvectors, diagonalization.

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) Recognizes the concepts of span, linear independence, basis and dimension and applies these concepts to various vector spaces and subspaces;
2 - Skills
Cognitive - Practical
1) Solve systems of linear equations using Gaussian elimination;
2) Calculates the determinant of a matrix and solves systems of linear equations using Cramer's rule;
3) Understands linear transformations and calculates matrix representations;
3 - Competences
Communication and Social Competence
Learning Competence
1) Calculates the eigenvalues and corresponding eigenvectors of a matrix.
Field Specific Competence
Competence to Work Independently and Take Responsibility

Ders Akış Planı

Week	Subject	Related Preparation
1)	Matrices and systems of linear equations	Lecture Notes
2)	Matrices and systems of linear equations	Lecture Notes
3)	Matrices and systems of linear equations	Lecture Notes
4)	Matrices and systems of linear equations	Lecture Notes
5)	Determinants	Lecture Notes
6)	Determinants	Lecture Notes
7)	Vector spaces	Lecture Notes
8)	Mid term	Lecture Notes
9)	Vector spaces	Lecture Notes
10)	Inner product spaces	Lecture Notes
11)	Linear transformations	Lecture Notes
12)	Linear transformations	Lecture Notes
13)	Eigenvalues and eigenvectors	Lecture Notes
14)	Eigenvalues and eigenvectors	Lecture Notes
15)	Final	Lecture Notes

Sources

Course Notes / Textbooks:	Linear Algebra and Its Applications, 5th edition/Global edition, David C. Lay et al., Pearson, 2016. Elementary Linear Algebra, Cengage Learning, 7th or 8th edition, Ron Larson, (e-book or hardcopy).
References:	1. Linear Algebra By MIT Open Courseware 2. Coding The Matrix By Philip Klein 3. Linear Algebra for Machine Learning By Applied AI Course 4. Deep Learning Book By Ian Goodfellow and Yoshua Bengio and Aaron Courville 5. Computational Linear Algebra for Coders By fast.ai 6. Basic Linear Algebra for Deep Learning By Niklas Donges 7. Linear Algebra By Khan Academy

Ders - Program Öğrenme Kazanım İlişkisi

Ders Öğrenme Kazanımları	1	2	4	3	5
Program Outcomes

Ders - Öğrenme Kazanımı İlişkisi

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

Program Outcomes

Level of Contribution

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Midterms	1	% 50
Final	1	% 50
total		% 100
PERCENTAGE OF SEMESTER WORK		% 50
PERCENTAGE OF FINAL WORK		% 50
total		% 100