TYPE OF COURSE UNIT	Elective Course
LEVEL OF COURSE UNIT	Master's Degree With Thesis
YEAR OF STUDY	-
SEMESTER	-
NUMBER OF ECTS CREDITS ALLOCATED	10
NAME OF LECTURER(S)	Professor Hamit Erdem
LEARNING OUTCOMES OF THE COURSE UNIT	At the end of this course, the students; 1) Explain the advantage and disadvantage of Intelligent Systems. 2) Gain an ability to know how choosing Intelligent Systems for real applications. 3) Learn the application of Intelligent Systems in real problems. 4) Gain an ability to choose the right method for a defined problem. 5) Gain an ability to develop programs aimed for Intelligent Systems employment using MATLAB or similar programing tools.
MODE OF DELIVERY	Face to face
PRE-REQUISITES OF THE COURSE	No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT	None
COURSE DEFINITION	Introduction to intelligent systems and soft computing, Fuzzy set theory, Fuzzy inference methods, Fuzzy clustering, Fuzzy logic control and application to nonlinear systems. Introduction to neural Networks, Adaptive Neuro-Fuzzy Inference system (ANFIS), Neuro-Fuzzy Classification (Nefclass), Introduction to intelligent optimization techniques and engineering applications. Course project
COURSE CONTENTS	WEEK TOPICS 1st Week Introduction to Intelligent Systems 2nd Week Fuzzy clustering theory 3rd Week Rule Based Control 4th Week Fuzzy logic control and its applications (P,PI,PD,PID) 5th Week Fuzzy logic control and its applications (P,PI,PD,PID) 6th Week Intelligent classification methods (NN,SVM) 7th Week Intelligent classification methods (NN,SVM) 8th Week Midterm Exam 9th Week Intelligent clustering methods (NN, Fuzzy Logic) 10th Week Neuro-Fuzzy inference systems (Anfis) 11th Week Neuro-Fuzzy classification (Nefclass) 12th Week Evolutionary optimization algorithms (GA, PSO) 13th Week Evolutionary optimization algorithms (GA, PSO) 14th Week Evolutionary optimization algorithm based fuzzy logic control
RECOMENDED OR REQUIRED READING	1. Artificial Intelligence: A Guide to Intelligent Systems", Negnevitsky, second edition 2. Lecture notes
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS	Lecture,Questions/Answers
ASSESSMENT METHODS AND CRITERIA	Quantity Percentage(%) Mid-term 1 30 Assignment 1 15 Project 1 25 Total(%) 70 Contribution of In-term Studies to Overall Grade(%) 70 Contribution of Final Examination to Overall Grade(%) 30 Total(%) 100
ECTS WORKLOAD	Activities Number Hours Workload Midterm exam 1 3 3 Preparation for Quiz 0 0 0 Individual or group work 14 8 112 Preparation for Final exam 1 40 40 Course hours 14 3 42 Preparation for Midterm exam 1 30 30 Laboratory (including preparation) 0 0 0 Final exam 1 3 3 Homework 2 35 70 Total Workload 300 Total Workload / 30 10 ECTS Credits of the Course 10
LANGUAGE OF INSTRUCTION	Turkish
WORK PLACEMENT(S)	No