Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1AIN421Fuzzy Logic3+0+03612.08.2025

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program ARTIFICIAL INTELLIGENCE ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course To teach the fundamental concepts in Fuzzy Logic and the development of intelligent systems based fuzzy inference systems.
Course Content General approach in fuzzy logic, fuzzy sets, relations and arithmetic. The relation between fuzzy logic and the other theories such as possibility and probability theory. Fuzzy inference systems. Hybrid methods in fuzzy inference. Fuzzy clustering. Examination of common application areas: Decision making, pattern recognition, data base, data mining. Fuzzy logic and artificial intelligence.
Course Methods and Techniques Lectures, discussion
Prerequisites and co-requisities ( BBM102 ) and ( BBM104 )
Course Coordinator None
Name of Lecturers Prof. Ebru Akçapınar Sezer
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Ross T.J., Fuzzy Logic with Engineering Applications, 3rd Ed., Wiley, 2004
Course Notes Ross T.J., Fuzzy Logic with Engineering Applications, 3rd Ed., Wiley, 2004.


Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Midterm Exam 1 % 50
Final examination 1 % 50
Total
2
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 3 42
Assignments 2 3 6
Preparation for Midterm Exam 1 30 30
General Exam Preparation 1 60 60
Total Work Load   Number of ECTS Credits 6 180

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 The ability of understanding of fuzz logic, fuzzy inference systems and to develop a FIS to solve a problem
2  
3  
4  
5  
6  
7  
8  

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 What is fuzzy logic
2 Fuzzy sets
3 Fuzzy sets
4 Fuzzy functions
5 Fuzzy relations
6 Midterm exam
7 Fuzzy inference systems
8 Mamdani Model
9 Sugeno Model
10 TSK Model
11 Midterm exam
12 Fuzzy Arithmatic
13 Hybrid methods
14 Hybrid methods
15 Final exam preparation
16 Final exam

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 5 5 5 1 1 1 1 1 1 1 1 1
C1
C2
C3
C4
C5
C6
C7
C8

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
  https://bilsis.hacettepe.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=2733688&lang=en