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Ders Tanımı

Ders Kodu Yarıyıl T+U Saat Kredi AKTS
ARTIFICIAL INTELLIGENCE APPLICATIONS IN MECHATRONIC ENGINEERING MEK 523 0 3 + 0 3 6
Ön Koşul Dersleri
Önerilen Seçmeli Dersler
Dersin Dili Türkçe
Dersin Seviyesi Yüksek Lisans
Dersin Türü SECMELI
Dersin Koordinatörü Prof.Dr. RAŞİT KÖKER
Dersi Verenler Dr.Öğr.Üyesi MEHMET AKİF KOÇ
Dersin Yardımcıları
Dersin Kategorisi Alanına Uygun Öğretim
Dersin Amacı
Students learn the basic principles of artificial intelligence and their applications to engineering problems as well as their in depth analyzes. Students learn the basic principles of fuzzy logical, experts systems and ANN in addition to their applications engineering.
Dersin İçeriği
Definition of artificial intelligence, The basic concepts and techniques, Expert Systems and engineering applications, Fuzzy logic and engineering applications, Genetic algorithms and application examples, Artificial neural networks: structure and basic elements of artificial neural networks, Engineering applications of artificial neural networks, Hybrid techniques (fuzzy-neural, fuzzy-genetic, etc.)
Dersin Öğrenme Çıktıları Öğretim Yöntemleri Ölçme Yöntemleri
1 - Definitions of artificial intelligence, basic concepts and techniques 14 - 2 - 1 - A - C -
2 - Experts systems ? application to mechatronics engineering 14 - 2 - 1 - C - A -
3 - Solutions to engineering problems using ANN 14 - 2 - 1 - F - C - A -
4 - Mechatronics engineering Applications by fuzzy logic 14 - 2 - 1 - A - C - F -
5 - Engineering applications by genetic algorithms 14 - 2 - 1 - F - C - A -
Öğretim Yöntemleri: 14:Self Study 2:Question-Answer 1:Lecture
Ölçme Yöntemleri: A:Testing C:Homework F:Performance Task

Ders Akışı

Hafta Konular ÖnHazırlık
1 İntroduction to Artificial intelligence technology
2 Expert Systems and engineering applications
3 Artificial Neural Networks: structure and basic elements of artificial neural networks
4 Multilayer Perceptrons - Backpropagation
5 Engineering applications of artificial neural networks
6 Engineering applications of artificial neural networks
7 Fuzzy Logic:The basic concepts
8 Features of Fuzzy logic
9 Fuzzy Logic and engineering applications
10 Basic theorem of genetic algorithm
11 Genetic algorithms and application examples
12 Midterm Exam
13 Presentations of student projects
14 Presentations of student projects

Kaynaklar

Ders Notu
Ders Kaynakları 1. Yapay Zeka, Vasif Nabiyev, Seçkin Yayınevi, 2010.
2. Mühendislikte yapay zeka uygulamaları, Ş.Sağıroğlu, E.Beşdok, M.Erler, Ufuk Yayınevi, 2003.
3. Neural Network Design, M. Hagan, 2002
4. .Fuzzy Logic and control, M. Jamshidi, Prentice Hall, 1993.

Döküman Paylaşımı


Dersin Program Çıktılarına Katkısı

No Program Öğrenme Çıktıları KatkıDüzeyi
1 2 3 4 5

Değerlendirme Sistemi

YARIYIL İÇİ ÇALIŞMALARI SIRA KATKI YÜZDESİ
Odev 1 25
Odev 2 25
Odev 3 25
Odev 4 25
Toplam 100
Yıliçinin Başarıya Oranı 40
Finalin Başarıya Oranı 60
Toplam 100

AKTS - İş Yükü

Etkinlik Sayısı Süresi(Saat) Toplam İş yükü(Saat)
Course Duration (Including the exam week: 16x Total course hours) 16 3 48
Hours for off-the-classroom study (Pre-study, practice) 16 4 64
Mid-terms 1 6 6
Assignment 1 10 10
Performance Task (Application) 1 10 10
Final examination 1 8 8
Toplam İş Yükü 146
Toplam İş Yükü /25(s) 5.84
Dersin AKTS Kredisi 5.84
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