EGE UNIVERSITY

FACULTY of ENGINEERING

COMPUTER ENGINEERING DEPARTMENT

 

 

 

 

2014-2015 SPRING SEMESTER

 

 

 

 

Course

 

435 İŞLEMSEL ZEKA (COMPUTATIONAL INTELLIGENCE) (3+0)

 

 

Instructor

 

 

Assoc. Prof. Dr. Aybars UĞUR

 

Course Place

 

EGE University Computer Engineering Department, Room B8

 

 

Course Time

 

Wednesday, 9:30 - 12:15

 

 

Assistant

 

 

Arif Erdal TAŞCI

 

 

 

 

 

 

 

 

 

Learning Outcomes

 

1.      To learn basic concepts of Computational Intelligence, mathematical and software background; to have ability to apply Computational Intelligence to problems. To recognize the role of Computational Intelligence in computer engineering, computer science and artificial intelligence.

 

2.      To introduce and to learn ability to use popular Computational Intelligence Tools. To enable to write simple Artificial Intelligence libraries in modern programming platforms (like Java and C#). To Develop Optimization, Prediction, Estimation, Classification and Recognition Projects.

 

3.      To develop Intelligent Software; To recognize that how the computers learn; To make efficient designs.

 

4.      To do research in state-of-the-art subjects of Computational Intelligence area; preparing and doing presentation. To gain experience in reading and writing papers in Computational Intelligence.

 

 

 

 

Aim

 

The goal of the course is to give the students:

 

·         Basic knowledge and practical experience about techniques like Artificial Neural Networks, Fuzzy Logic, Genetic Algorithms and Swarm Intelligence based on Computational Intelligence and Soft Computing;

 

·         with an understanding of the role of Computational Intelligence in computer engineering, computer science and Artificial Intelligence.

 

 

Course Contents

 

Computational Intelligence, Artificial Intelligence, Search, Heuristic Search, Local Search, Introduction to Artificial Neural Networks, Artificial Neuron, Structure and Basic Elements of ANN, Machine Learning, Supervised, Reinforcement and Unsupervised Learning, Single Layer Perceptons, Multi Layer Perceptrons (MLP), Other Neural Models, Application Areas of ANN, Object Recognition, Fuzzy Logic, Genetic Algorithms, Swarm Intelligence.

 

 

Prerequisites

 

Basic knowledge of Probability, Statistics and Calculus.

Java / C# or Matlab programming experience.

 

 

 

 

Textbook

 

 

§  Russell, S.J. And Norvig, P., “Artificial Intelligence : A Modern Approach, Third Edition”, Prentice-Hall, 2009. (AIMA)

 

§  Prof. Dr. Ercan Öztemel,  2012, “Yapay Sinir Ağları”, Papatya Yayıncılık, 232s.

§  Prof. Dr. Çetin Elmas, 2011, "Yapay Zeka Uygulamaları", 2. Baskı, Seçkin Yayıncılık, 425 s.

 

 

Reference Books

 

§  Haykin, Simon, 1998, “Neural Networks: A Comprehensive Foundation (2nd Edition)”, Prentice-Hall, 842p

§  Vasif Nabiyev , Yapay Zeka: İnsan – Bilgisayar Etkileşimi, 3. baskı, 752 s., Seçkin, Ankara, 2010.

§  Okyay Kaynak ve M. Önder Efe, “Yapay Sinir Ağları ve Uygulamaları”, Boğaziçi Üniversitesi Yayınevi, 141s.

§  Şeref Sağıroğlu, Erkan Beşdok, Mehmet Erler, 2003, “Mühendislikte Yapay Zeka Uygulamaları - I : Yapay Sinir Ağları”, Ufuk Yayıncılık, 426s.

 

Also Sample Papers will be given.

 

 

Grading

 

 

Activities
[
Projects 1,2; Presentation]

25 %

Midterm Exam

25 %

Final Exam

50 %