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Information
| Code | ISB304 |
| Name | Introduction to Artificial Neural Networks |
| Term | 2024-2025 Academic Year |
| Semester | 6. Semester |
| Duration (T+A) | 2-0 (T-A) (17 Week) |
| ECTS | 3 ECTS |
| National Credit | 2 National Credit |
| Teaching Language | Türkçe |
| Level | Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. GÜLESEN ÜSTÜNDAĞ ŞİRAY |
| Course Instructor |
1 2 |
Course Goal / Objective
The aim of this course is to introduce the artificial neural network and show how to analyze data using artificial neural network for students.
Course Content
Introduction, Basics of ANN, Learning Processes, Some Optimization Algorithms, Single-Layer Perceptrons, Multilayer Perceptrons constitutes the content of this course
Course Precondition
None
Resources
Introduction to Machine Learning (2010), 2nd edition,The MIT Press. Ethem ALPAYDIN Yapay Zeka Uygulamaları, Seçkin Yayıncılık. Prof. Dr. Çetin ELMAS Yapay Sinir Ağları, Nobel Yayıncılık. Prof. Dr. Erol EĞRİOĞLU, Prof.Dr. Ufuk YOLCU, Prof.Dr. Eren BAŞ Yapay Sinir Ağları, Papatya Yayıncılık. Prof.Dr. Ercan ÖZTEMEL
Notes
Internet
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explain the basics of artificial neural networks (ANN). |
| LO02 | Defines the components of ANN. |
| LO03 | Builds an artificial neural network. |
| LO04 | Compares the effects of different activation functions on ANN. |
| LO05 | Distinguishes between single-layer and multi-layer perceptrons. |
| LO06 | Uses difference optimization algorithms to build an ANN. |
| LO07 | Performs data analysis with ANN. |
| LO08 | Distinguish the difference between supervised learning and unsupervised learning. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Explain the essence fundamentals and concepts in the field of Statistics | |
| PLO02 | Bilgi - Kuramsal, Olgusal | Emphasize the importance of Statistics in life | 4 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Define basic principles and concepts in the field of Law and Economics | |
| PLO04 | Bilgi - Kuramsal, Olgusal | Produce numeric and statistical solutions in order to overcome the problems | |
| PLO05 | Bilgi - Kuramsal, Olgusal | Use proper methods and techniques to gather and/or to arrange the data | 4 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Utilize computer programs and builds models, solves problems, does analyses and comments about problems concerning randomization | |
| PLO07 | Bilgi - Kuramsal, Olgusal | Apply the statistical analyze methods | 4 |
| PLO08 | Bilgi - Kuramsal, Olgusal | Make statistical inference (estimation, hypothesis tests etc.) | |
| PLO09 | Bilgi - Kuramsal, Olgusal | Generate solutions for the problems in other disciplines by using statistical techniques and gain insight | |
| PLO10 | Bilgi - Kuramsal, Olgusal | Discover the visual, database and web programming techniques and posses the ability of writing programs | |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Distinguish the difference between the statistical methods | |
| PLO12 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Make oral and visual presentation for the results of statistical methods | |
| PLO13 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Have capability on effective and productive work in a group and individually | |
| PLO14 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Professional development in accordance with their interests and abilities, as well as the scientific, cultural, artistic and social fields, constantly improve themselves by identifying training needs | |
| PLO15 | Yetkinlikler - Öğrenme Yetkinliği | Develop scientific and ethical values in the fields of statistics-and scientific data collection |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to ANN, General usage areas, advantages and disadvantages of ANN | Source reading | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 2 | Components of ANN | Source reading | Öğretim Yöntemleri: Soru-Cevap, Alıştırma ve Uygulama, Anlatım |
| 3 | Single Layer Perceptrons | Source reading | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 4 | Activation Functions | Source reading | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Problem Çözme, Alıştırma ve Uygulama |
| 5 | Learning Algorithms | Source reading | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama |
| 6 | Optimization Algorithms | Source reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Problem Çözme |
| 7 | Optimization Algorithms II | Source reading | Öğretim Yöntemleri: Anlatım, Problem Çözme |
| 8 | Mid-Term Exam | Review the topics discussed in the lecture notes and sources | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Multi Layer Perceptrons | Source reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Problem Çözme |
| 10 | Multi Layer Perceptrons II | Source reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Problem Çözme |
| 11 | Supervised Learning | Source reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Problem Çözme |
| 12 | Unsupervised Learning | Source reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Problem Çözme |
| 13 | ANN Applications with Matlab | Doing applications in MATLAB | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 14 | ANN Applications with Matlab II | Doing applications in MATLAB | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 15 | ANN Applications with Matlab III | Doing applications in MATLAB | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 16 | Term Exams | Review the topics discussed in the lecture notes and sources | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Review the topics discussed in the lecture notes and sources | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 2 | 28 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 1 | 14 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 1 | 7 | 7 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 7 | 7 |
| Final Exam | 1 | 12 | 12 |
| Total Workload (Hour) | 68 | ||
| Total Workload / 25 (h) | 2,72 | ||
| ECTS | 3 ECTS | ||