COURSE INTRODUCTION AND APPLICATION INFORMATION


Course Name
Linear Algebra I
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
MATH 105
Fall
3
0
3
5
Prerequisites
None
Course Language
English
Course Type
Required
Course Level
First Cycle
Mode of Delivery face to face
Teaching Methods and Techniques of the Course Problem Solving
Q&A
Lecturing / Presentation
Course Coordinator
Course Lecturer(s)
Assistant(s)
Course Objectives This course aims to teach students to solve linear systems of equations using several methods, introduce linear independence and linear transformations, and find inverses and determinants of matrices.
Learning Outcomes The students who succeeded in this course;
  • solve linear systems of equations using Gauss elimination method.
  • define linear independence and linear transformations.
  • apply linear systems of equations in several fields.
  • find the inverse of a matrix using several method.
  • find matrix factorizations.
  • calculate determinant of a matrix.
  • solve linear system of equations using Cramer’s rule.
Course Description This course introduces linear system of equations, vector and matrix equations, linear independence, linear transformation, determinants and applications in various fields.
Related Sustainable Development Goals

 



Course Category

Core Courses
X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Required Materials
1 Systems of linear equations. Row reduction and echelon forms David C.Lay, Stephan R.Lay and Judi J. McDonald, "Linear Algebra and Its Applications", 5th ed. (Pearson, 2015). Section 1.1, 1.2
2 Row reduction and echelon forms, vector equations David C.Lay, Stephan R.Lay and Judi J. McDonald, "Linear Algebra and Its Applications", 5th ed. (Pearson, 2015). Section 1.2, 1.3
3 The matrix equation, solution sets of linear systems David C.Lay, Stephan R.Lay and Judi J. McDonald, "Linear Algebra and Its Applications", 5th ed. (Pearson, 2015). Section 1.4, 1.5
4 Applications of linear systems. Linear independence David C.Lay, Stephan R.Lay and Judi J. McDonald, "Linear Algebra and Its Applications", 5th ed. (Pearson, 2015). Section 1.6, 1.7
5 Introduction to linear transformations The matrix of a linear transformation David C.Lay, Stephan R.Lay and Judi J. McDonald, "Linear Algebra and Its Applications", 5th ed. (Pearson, 2015). Section 1.8, 1.9
6 Linear models in business, science, and engineering David C.Lay, Stephan R.Lay and Judi J. McDonald, "Linear Algebra and Its Applications", 5th ed. (Pearson, 2015). Section 1.10
7 Midterm Exam
8 Matrix Operations, The inverse of a matrix David C.Lay, Stephan R.Lay and Judi J. McDonald, "Linear Algebra and Its Applications", 5th ed. (Pearson, 2015). Section 2.1, 2.2
9 Characterizations of invertible matrices David C.Lay, Stephan R.Lay and Judi J. McDonald, "Linear Algebra and Its Applications", 5th ed. (Pearson, 2015). Section 2.3
10 Matrix factorizations David C.Lay, Stephan R.Lay and Judi J. McDonald, "Linear Algebra and Its Applications", 5th ed. (Pearson, 2015). Section 2.5
11 The Leontief Input-Output Model David C.Lay, Stephan R.Lay and Judi J. McDonald, "Linear Algebra and Its Applications", 5th ed. (Pearson, 2015). Section 2.6
12 Subspaces of R^n, dimension and rank David C.Lay, Stephan R.Lay and Judi J. McDonald, "Linear Algebra and Its Applications", 5th ed. (Pearson, 2015). Section 2.8, 2.9
13 Introduction to determinants, Properties of determinants David C.Lay, Stephan R.Lay and Judi J. McDonald, "Linear Algebra and Its Applications", 5th ed. (Pearson, 2015). Section 3.1, 3.2.
14 Cramer’s rule, volume and linear transformations David C.Lay, Stephan R.Lay and Judi J. McDonald, "Linear Algebra and Its Applications", 5th ed. (Pearson, 2015). Section 3.3
15 Semester review
16 Final exam
Course Notes/Textbooks

David C.Lay, Stephan R.Lay and Judi J. McDonald, "Linear Algebra and Its Applications", 5th ed. (Pearson,

2015). ISBN-13:978-0321982384

 
Suggested Readings/Materials

 Howard Anton, Chris Rorres, ''Elementary Linear Algebra'' Publisher:Wiley, 9th Edition,2005. ISBN-13: 978-0471669593 

Seymour Lipschutz, ''Linear Algebra'',  Shaum’s Outline Series, 2nd Edition.2011, ISBN-13:9780070380073

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
5
30
Portfolio
Homework / Assignments
Presentation / Jury
Project
Seminar / Workshop
Oral Exam
Midterm
1
30
Final Exam
1
40
Total

Weighting of Semester Activities on the Final Grade
6
60
Weighting of End-of-Semester Activities on the Final Grade
1
40
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: 16 x total hours)
16
Study Hours Out of Class
14
3
42
Field Work
Quizzes / Studio Critiques
5
6
Portfolio
Homework / Assignments
Presentation / Jury
Project
Seminar / Workshop
Oral Exam
Midterms
1
12
Final Exams
1
18
    Total
150

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To be able to have a grasp of basic mathematics, applied mathematics or theories and applications of statistics.

X
2

To be able to use advanced theoretical and applied knowledge, interpret and evaluate data, define and analyze problems, develop solutions based on research and proofs by using acquired advanced knowledge and skills within the fields of mathematics or statistics.

X
3

To be able to apply mathematics or statistics in real life phenomena with interdisciplinary approach and discover their potentials.

4

To be able to evaluate the knowledge and skills acquired at an advanced level in the field with a critical approach and develop positive attitude towards lifelong learning.

X
5

To be able to share the ideas and solution proposals to problems on issues in the field with professionals, non-professionals.

X
6

To be able to take responsibility both as a team member or individual in order to solve unexpected complex problems faced within the implementations in the field, planning and managing activities towards the development of subordinates in the framework of a project.

7

To be able to use informatics and communication technologies with at least a minimum level of European Computer Driving License Advanced Level software knowledge.

8

To be able to act in accordance with social, scientific, cultural and ethical values on the stages of gathering, implementation and release of the results of data related to the field.

9

To be able to possess sufficient consciousness about the issues of universality of social rights, social justice, quality, cultural values and also environmental protection, worker's health and security.

10

To be able to connect concrete events and transfer solutions, collect data, analyze and interpret results using scientific methods and having a way of abstract thinking.

11

To be able to collect data in the areas of Mathematics or Statistics and communicate with colleagues in a foreign language.

12

To be able to speak a second foreign language at a medium level of fluency efficiently.

13

To be able to relate the knowledge accumulated throughout the human history to their field of expertise.

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest