EGE UNIVERSITY

FACULTY of ENGINEERING

COMPUTER ENGINEERING DEPARTMENT

2019-2020 SPRING SEMESTER

Course

435 İŞLEMSEL ZEKA ve DERİN ÖĞRENME
(COMPUTATIONAL INTELLIGENCE and DEEP LEARNING) (3+0)

Instructor

Prof. Dr. Aybars UĞUR

Course Place

EGE University Computer Engineering Department, Room B8

Course Time

Wednesday, 9:30 - 12:15

Assistants

Research Assistant Dr. Osman GÖKALP

Office Hour: Thu 14:15-15:00

Research Assistant Dr. A. Erdal TAŞCI

Office Hour: Wed 15:15-16:00

Learning Outcomes

1. To learn basic concepts of Computational Intelligence and Deep Learning, 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 and Deep Learning libraries / 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 and Deep Learning areas; preparing and doing presentation. To gain experience in reading and writing papers in Machine Learning.

Aim

The goal of the course is to give the students:

· Basic knowledge and practical experience about techniques like Artificial Neural Networks, Deep Learning, 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, Derin Öğrenme, Application Areas of ANN, Object Recognition, Fuzzy Logic, Genetic Algorithms, Swarm Intelligence.

Prerequisites

Basic knowledge of Probability, Statistics and Calculus.

Java / C# / Python 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, 2018, "Yapay Zeka Uygulamaları", 4. Baskı, Seçkin Yayıncılık, 479 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 %