Syllabus ( MATH 116 )
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Basic information
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| Course title: |
Linear Algebra |
| Course code: |
MATH 116 |
| Lecturer: |
Assoc. Prof. Dr. Nursel EREY
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| ECTS credits: |
5 |
| GTU credits: |
3 (3+0+0) |
| Year, Semester: |
1, Spring |
| Level of course: |
First Cycle (Undergraduate) |
| Type of course: |
Compulsory
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| Language of instruction: |
English
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| Mode of delivery: |
Face to face
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| Pre- and co-requisites: |
None |
| Professional practice: |
No |
| Purpose of the course: |
To teach the solution techniques for linear systems, and the mathematical concept of linear independence.
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Learning outcomes
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Upon successful completion of this course, students will be able to:
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Learning to represent the linear systems with matrix equations and to solve linear equations with matrix methods
Contribution to Program Outcomes
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Having the knowledge about the scope, applications, history, problems, and methodology of mathematics that are useful to humanity both as a scientific and as an intellectual discipline.
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Communicating between mathematics and other disciplines, and building mathematical models for interdisciplinary problems.
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Describing, formulating, and analyzing real-life problems using mathematical and statistical techniques.
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Having improved abilities in mathematics communications, problem-solving, and brainstorming skills.
Method of assessment
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Written exam
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Finding eigenvalues and eigenvectors for Linear Equations Systems.
Contribution to Program Outcomes
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Having the knowledge about the scope, applications, history, problems, and methodology of mathematics that are useful to humanity both as a scientific and as an intellectual discipline.
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Communicating between mathematics and other disciplines, and building mathematical models for interdisciplinary problems.
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Describing, formulating, and analyzing real-life problems using mathematical and statistical techniques.
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Having improved abilities in mathematics communications, problem-solving, and brainstorming skills.
Method of assessment
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Written exam
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Understanding linear independence and vector spaces
Contribution to Program Outcomes
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Having the knowledge about the scope, applications, history, problems, and methodology of mathematics that are useful to humanity both as a scientific and as an intellectual discipline.
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Communicating between mathematics and other disciplines, and building mathematical models for interdisciplinary problems.
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Describing, formulating, and analyzing real-life problems using mathematical and statistical techniques.
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Having improved abilities in mathematics communications, problem-solving, and brainstorming skills.
Method of assessment
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Written exam
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Contents
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| Week 1: |
Matrices, systems of linear equations, matrix operations |
| Week 2: |
Determinants, matrix inverses. |
| Week 3: |
Echelon form, reduced echelon form, solutions of linear equations |
| Week 4: |
Vector space. Linear independence. |
| Week 5: |
Bases for vector spaces. |
| Week 6: |
Change of basis. |
| Week 7: |
Echelon forms and LU decomposition. |
| Week 8: |
Matrix rank, row space column space , null space. |
| Week 9: |
Linear transformations. |
| Week 10: |
Inner product spaces, Orthogonal bases. |
| Week 11: |
Eigenvalues and eigenvectors |
| Week 12: |
Midterm exam and solutions. |
| Week 13: |
Diagonalization |
| Week 14: |
Gram-Schmidt method. |
| Week 15*: |
- |
| Week 16*: |
Final Exam. |
| Textbooks and materials: |
Linear Algebra with Applications, Steven J. Leon, 9th ed. 2015, Pearson - Prentice Hall. |
| Recommended readings: |
Linear Algebra and its applications, David C. Lay, 2012. |
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* Between 15th and 16th weeks is there a free week for students to prepare for final exam.
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Assessment
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| Method of assessment |
Week number |
Weight (%) |
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| Mid-terms: |
12 |
40 |
| Other in-term studies: |
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0 |
| Project: |
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0 |
| Homework: |
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0 |
| Quiz: |
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0 |
| Final exam: |
16 |
60 |
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Total weight: |
(%) |
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Workload
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| Activity |
Duration (Hours per week) |
Total number of weeks |
Total hours in term |
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| Courses (Face-to-face teaching): |
3 |
14 |
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| Own studies outside class: |
4 |
14 |
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| Practice, Recitation: |
0 |
0 |
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| Homework: |
3 |
2 |
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| Term project: |
0 |
0 |
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| Term project presentation: |
0 |
0 |
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| Quiz: |
0 |
0 |
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| Own study for mid-term exam: |
5 |
1 |
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| Mid-term: |
2 |
1 |
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| Personal studies for final exam: |
6 |
1 |
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| Final exam: |
2 |
1 |
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Total workload: |
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Total ECTS credits: |
* |
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* ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
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