Description of Individual Course Units
Course Unit CodeCourse Unit TitleType of Course UnitYear of StudySemesterNumber of ECTS Credits
İST311FUZZY PROBABILITY AND STATISTICSElective354
Level of Course Unit
First Cycle
Objectives of the Course
Fuzzy logic models human logic inference systems in a linear and uncomplicated manner. In the course, fundamentals of fuzzy logic are mentioned. Moreover, statistical methods that are developed with the use of fuzzy logic are presented.
Name of Lecturer(s)
Doç. Dr. Ali MERT
Learning Outcomes
1To learn the fundamentals of fuzzy logic
2To learn the difference between fuzzy and classical statistical methods.
3To learn how fuzzy statistical methods are applied.
Mode of Delivery
Face to Face
Prerequisites and co-requisities
Recommended Optional Programme Components
Course Contents
Fuzzy sets and their statement types. Membership functions and their types. Definition of fuzzy number and its statement types. Operations on fuzzy sets and fuzzy arithmetic. Defuzzification methods. Fuzzy linear programming problem and its solution methods. Systems operating with fuzzy rules. Fuzzy probability theory. Fuzzy random variable and its types. Fuzzy estimators of population parameters. Fuzzy hypotheses test. Fuzzy regression analysis. Recent applications concerning fuzzy systems.
Weekly Detailed Course Contents
WeekTheoreticalPracticeLaboratory
1Recent Applications Concerning Fuzzy Systems
2Fuzzy Sets and Fundamental Definitions
3Membership Functions and Types of Fuzzy Number.
4Operations on Fuzzy Set and Fuzzy Arithmetic
5Defuzzification Strategies.
6Fuzzy Linear Programming
7Fuzzy Linear Programming
8Mid-Term
9Fuzzy Rule-Based Systems
10Fuzzy Probability Theory
11Discrete Fuzzy Random Variables
12Continuous Fuzzy Random Variables
13Fuzzy Estimator of Population Parameters
14Fuzzy Hypotheses Test
15Fuzzy Hypotheses Test
16Fuzzy Regression Analysis
Recommended or Required Reading
J.J. Buckley, Fuzzy Probability and Statistics, Springer – Verlag Berlin, 2006. H.T. Nguyen and B. Wu, Fundamentals of Statistics with Fuzzy Data, Springer – Verlag Berlin, 2006. D. Dubois and H. Prade (edt.), Fundamentals of Fuzzy Sets, Kluwer Academic Publishers, 2000.
Planned Learning Activities and Teaching Methods
Assessment Methods and Criteria
Term (or Year) Learning ActivitiesQuantityWeight
Midterm Examination165
Project Presentation135
SUM100
End Of Term (or Year) Learning ActivitiesQuantityWeight
Final Examination1100
SUM100
Term (or Year) Learning Activities40
End Of Term (or Year) Learning Activities60
SUM100
Language of Instruction
Lisan Kodları
Work Placement(s)
Workload Calculation
ActivitiesNumberTime (hours)Total Work Load (hours)
Midterm Examination111
Final Examination122
Attending Lectures14342
Individual Study for Mid term Examination12020
Individual Study for Final Examination12020
Reading20120
TOTAL WORKLOAD (hours)105
Contribution of Learning Outcomes to Programme Outcomes
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* Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High
 
Ege University, Bornova - İzmir / TURKEY • Phone: +90 232 311 10 10 • e-mail: intrec@mail.ege.edu.tr