TÜRKÇE
ENGLISH
Information
| Unit | FACULTY OF SCIENCE AND LETTERS |
| ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING PR. (ENGLISH) | |
| Code | YZZ206 |
| Name | Numerical Analysis |
| Term | 2026-2027 Academic Year |
| Semester | 4. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Lisans Dersi |
| Type | Normal |
| Label | FE Field Education Courses C Compulsory |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. YUSUF ALPER KAPLAN |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
To introduce students to techniques for the numerical approximation of mathematical problems, and to the analysis of these techniques.
Course Content
Surveys and applications of numerical techniques related to matrix inversion, systems of linear equations and optimization, finite difference expressions, interpolation and approximation, numerical differentiation and integration.
Course Precondition
no prerequisites
Resources
Numerical Analysis David Kincaid Ward Cheney
Notes
Numerical Analysis Timothy Sauer
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | perform an error analysis for various numerical methods |
| LO02 | Solve an algebraic equation using an appropriate numerical method. |
| LO03 | Solve a linear system of equation using an appropriate numerical method. |
| LO04 | Approximate a function using a numerical method. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | It provides a broad range of knowledge about fundamental Computer Science concepts, algorithms and data structures. | |
| PLO02 | Bilgi - Kuramsal, Olgusal | Learns basic computer topics such as software development, programming languages, and database management. | |
| PLO03 | Bilgi - Kuramsal, Olgusal | Understands advanced computing fields such as data science, artificial intelligence, and machine learning. | |
| PLO04 | - | Learn about topics such as computer networks, cyber security, and database design. | |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Develops skills in designing, implementing and analyzing algorithms. | 5 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Gains the ability to use different programming languages effectively | |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Learns data analysis, database management and big data processing skills. | |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Gains practical experience by working on software development projects. | |
| PLO09 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Strengthens collaboration and communication skills within the team. | |
| PLO10 | Yetkinlikler - Alana Özgü Yetkinlik | It provides a mindset open to technological innovations. | |
| PLO11 | Yetkinlikler - Öğrenme Yetkinliği | Encourages continuous learning and self-improvement competence. | |
| PLO12 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Develops the ability to solve complex problems. | 5 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Computer arithmetic | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 2 | Floating Point Numbers and Roundoff Errors | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 3 | Absolute and Relative Errors | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 4 | Solutions of nonlinear equations | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 5 | Bisection Method | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 6 | Newton's Method | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 7 | Solutions of linear equations | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | Ölçme Yöntemleri: Yazılı Sınav |
|
| 9 | Matrix algebra | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 10 | The LU and Cholesky Factorizations | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 11 | Pivoting and Constructing and Algorithm | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 12 | Function approximations | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 13 | Polynomial Interpolation | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 14 | Divided Differences | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 15 | Hermite Interpolation, Numerical Differentiation and Integration | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 16 | Term Exams | Ölçme Yöntemleri: Yazılı Sınav |
|
| 17 | Term Exams | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 30 | 30 |
| Total Workload (Hour) | 157 | ||
| Total Workload / 25 (h) | 6,28 | ||
| ECTS | 6 ECTS | ||