TR
11111

COURSE INTRODUCTION AND APPLICATION INFORMATION

se.cs.ieu.edu.tr

Course Name
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
Fall
Prerequisites
None
Course Language
Course Type
Required
Course Level
-
Mode of Delivery -
Teaching Methods and Techniques of the Course Problem Solving
Q&A
Course Coordinator
Course Lecturer(s)
Assistant(s)
Course Objectives
Learning Outcomes The students who succeeded in this course;
  • will be able to determine if a linear system is consistent and solve the system by Gaussian elimination method
  • will be able to determine if a linear system is consistent and solve the system by Gaussian elimination method
  • will be able to apply the basic techniques of matrix algebra, including finding the inverse of an invertible matrix using Gauss-Jordan elimination
  • will be able to apply basic concepts of linear models to various applications
  • will be able to find dimension and basis vectors of linear vector spaces and subspaces
Course Description

 



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. Linear Algebra and Its Applications, David C. Lay, Steven R. Lay, Judi J. McDonald, Pearson, 5th Edition,Section 1.1, 1.2.
2 Vector Equations. Solution Sets of Linear Systems. Linear Algebra and Its Applications, David C. Lay, Steven R. Lay, Judi J. McDonald, Pearson, 5th Edition, Section 1.3, 1.5,
3 Applications of Linear Systems. Linear Independence. Linear Algebra and Its Applications, David C. Lay, Steven R. Lay, Judi J. McDonald, Pearson, 5th Edition, Section 1.6, 1.7
4 Introduction to Linear Transformations. Linear Models in Business, Science, and Engineering Linear Algebra and Its Applications, David C. Lay, Steven R. Lay, Judi J. McDonald, Pearson, 5th Edition, Section 1.8, 1.10
5 Matrix Operations. The Inverse of a Matrix. Characterizations of Invertible Matrices Linear Algebra and Its Applications, David C. Lay, Steven R. Lay, Judi J. McDonald, Pearson, 5th Edition, Section 2.1, 2.2, 2.3
6 Partitioned Matrices. Matrix Factorizations. The Leontief Input-Output Model. Midterm Exam 1. Linear Algebra and Its Applications, David C. Lay, Steven R. Lay, Judi J. McDonald, Pearson, 5th Edition, Section 2.4, 2.5, 2.6
7 Applications to Computer Graphics. Introduction of Determinants. Properties of Determinants Linear Algebra and Its Applications, David C. Lay, Steven R. Lay, Judi J. McDonald, Pearson, 5th Edition, Section 2.7, 3.1, 3.2
8 Cramer’s Rule. Vector Spaces and Subspaces. Null Spaces, Column Spaces and Linear Transformations Linear Algebra and Its Applications, David C. Lay, Steven R. Lay, Judi J. McDonald, Pearson, 5th Edition, Section 3.3, 4.1 4.2
9 Linearly Independent Sets; Bases. The Dimension of a vector space. Rank Linear Algebra and Its Applications, David C. Lay, Steven R. Lay, Judi J. McDonald, Pearson, 5th Edition, Section 4.3, 4.5, 4.6
10 Coordinate Systems. Change of a Basis. Applications to Difference Equations. Linear Algebra and Its Applications, David C. Lay, Steven R. Lay, Judi J. McDonald, Pearson, 5th Edition, Section 4.4, 4.7, 4.8
11 Applications to Markov Chains. Eigenvectors and Eigenvalues. The Characteristic Equation. Linear Algebra and Its Applications, David C. Lay, Steven R. Lay, Judi J. McDonald, Pearson, 5th Edition, Section 4.9, 5.1, 5.2
12 Diagonalization. Inner Product, Length and Orthogonality. Midterm Linear Algebra and Its Applications, David C. Lay, Steven R. Lay, Judi J. McDonald, Pearson, 5th Edition, Section 5.3, 6.1
13 Orthogonal Projections. Orthogonal Sets. Linear Algebra and Its Applications, David C. Lay, Steven R. Lay, Judi J. McDonald, Pearson, 5th Edition, Section 6.2, 6.3
14 The Gram-Schmidt Process. Least-Squares Problems. Linear Algebra and Its Applications, David C. Lay, Steven R. Lay, Judi J. McDonald, Pearson, 5th Edition, Section 6.4, 6.5
15 Review
16 Final exam.
Course Notes/Textbooks Linear Algebra and Its Applications, David C. Lay, Steven R. Lay, Judi J. McDonald, Pearson, 5th Edition.
Suggested Readings/Materials 1) Elementary Linear Algebra, Howard Anton, Chris Rorres, Wiley, 9th Edition. 2) Linear Algebra, Seymour Lipschutz, Shaum’s Outline Series, 2nd Edition.

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
-
-
Portfolio
Homework / Assignments
14
30
Presentation / Jury
Project
Seminar / Workshop
Oral Exam
Midterm
1
30
Final Exam
1
40
Total

Weighting of Semester Activities on the Final Grade
15
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
16
3
Field Work
Quizzes / Studio Critiques
-
-
Portfolio
Homework / Assignments
14
1
Presentation / Jury
Project
Seminar / Workshop
Oral Exam
Midterms
2
20
Final Exams
1
30
    Total
180

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
 Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1 Be able to define problems in real life by identifying functional and nonfunctional requirements that the software is to execute
2 Be able to design and analyze software at component, subsystem, and software architecture level
3 Be able to develop software by coding, verifying, doing unit testing and debugging
4 Be able to verify software by testing its behaviour, execution conditions, and expected results
5 Be able to maintain software due to working environment changes, new user demands and the emergence of software errors that occur during operation
6 Be able to monitor and control changes in the software, the integration of software with other software systems, and plan to release software versions systematically
7 To have knowledge in the area of software requirements understanding, process planning, output specification, resource planning, risk management and quality planning
8 Be able to identify, evaluate, measure and manage changes in software development by applying software engineering processes
9 Be able to use various tools and methods to do the software requirements, design, development, testing and maintenance
10 To have knowledge of basic quality metrics, software life cycle processes, software quality, quality model characteristics, and be able to use them to develop, verify and test software
11 To have knowledge in other disciplines that have common boundaries with software engineering such as computer engineering, management, mathematics, project management, quality management, software ergonomics and systems engineering X
12 Be able to grasp software engineering culture and concept of ethics, and have the basic information of applying them in the software engineering
13

Be able to use a foreign language to follow related field publications and communicate with colleagues

 X

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

 

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