EGE UNIVERSITY

GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES
ENGINEERING SCIENCES

COMPUTER ENGINEERING DEPARTMENT

 

 

 

 

2015-2016 SPRING SEMESTER

 

 

 

 

Course

 

 

624 INTELLIGENT SYSTEMS (3+0)

 

 

Instructor

 

 

Prof. Dr. Aybars UĞUR

 

Course Place and Time

 

 

EGE University Computer Engineering Department B1 Room

(Tuesday, 13:15-16:00)

 

 

Learning Outcomes

 

 

 

  • To learn basic concepts of Intelligent Systems, mathematical and software background; to have ability to apply Intelligent Systems to problems. To have both a general “breadth” knowledge of AI techniques, plus a deeper specialized knowledge of one particular sub-area within AI; how to combine or integrate them.
  • To be able to design and implement own Intelligent system (IS)
  • To learn ability to use and integrate Software Tools in Artificial Neural Networks, Genetic Algorithms, Fuzzy Logic, Expert Systems.
  • To do research in state-of-the-art subjects of Intelligent Systems area; preparing and doing presentation. To gain experience in reading and writing papers in Intelligent Systems.

 

 

Description

 

Introduction to Intelligent Systems, Artificial Neural Networks, Evolutionary Computation, Fuzzy Logic, Expert Systems, Hybrid Intelligent Systems, Fuzzy Expert systems, Neural Expert Systems, Neuro-fuzzy Systems, Evolutionary Neural Networks.

 

Syllabus

 

TOPICS

Intelligent Systems

Rule-based Expert Systems

Fuzzy Expert Systems

Frame-based Expert Systems

Machine Learning

Artificial Neural Networks

Evolutionary Computation

Metaheuristic Algorithms

Hybrid Intelligent Systems

Knowledge Engineering and Data Mining

Computer Vision

Robotics

 

 

 

Textbook

 

 

Michael Negnevitsky, “Artificial Intelligence : A Guide to Intelligent Systems (3rd Edition)”, Addison Wesley, 2011.

 

 

Reference Books

 

  • Mircea Negoita, Daniel Neagu, Vasile Palade, “Computational Intelligence: Engineering of Hybrid Systems (Studies in Fuzziness and Soft Computing)”, Springer, 2005.
  • Computational Intelligence: A Logical Approach. Poole, Mackworth and Goebel. Oxford University Press, 1998.
  • Neuro-Fuzzy and Soft Computing. J.S.R. Jang, C.T. Sun, E.Mizutani. Prentice Hall 1997.
  • Seven methods for transforming corporate data into business intelligence V Dhar & R Stein Prentice Hall 1997.

 

Also Sample Papers will be given.

 

 

Links

 

http://en.wikipedia.org/wiki/Hybrid_intelligent_system

http://www.slideshare.net/ikensolutions/hybrid-intelligent-systems-presentation

 

 

Prerequisites

 

Artificial Intelligence Basics:  Problem solving, state space search, machine learning principles, pattern recognition, fundamentals of computer vision. Basic knowledge of probability, statistics, calculus and linear algebra.

 

 

Grading

 

Midterm Activities (50%)

·         Project (+ Paper) (20%)

·         Take-Home (20%)

·         Presentation and Document (.ppt) (10%)

 

Final Exam (50%)

 

For the Take-Home and reports, students will be allowed a total of 5 (five) late days; each additional late day will incur a 10% penalty.