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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS BIL 3130 FUZZY DECISION SYSTEMS ELECTIVE 2 2 0 5

Offered By

Computer Science

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSISTANT PROFESSOR AYŞE ÖVGÜ KINAY

Offered to

Computer Science

Course Objective

The objective of the course is to construct fuzzy decision systems, to teach the basic structure of fuzzy logic and to gain ability of design of fuzzy logic controllers.

Learning Outcomes of the Course Unit

1   Be able to construct fuzzy decision making models. 2   Be able to design fuzzy inference systems. 3   Be able to use cluster analysis. 4   Be able to perform fuzzy pattern recognition procedures and create fuzzy decision trees. 5   Be able to decision making with fuzzy information.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Fuzzy Inference Systems 2 Fuzzy Inference Systems (cont.) 3 Fuzzy Inference Systems (cont.) 4 Fuzzy Inference Systems (cont.) Matlab FIS editor 5 Multi-criteria decision-making problems Analytic Hierarchy Process (AHP) 6 Fuzzy Analytical Hierarchy Process (FAHP) (Geometric Method, Magnitude Based Fuzzy AHP Method) 7 TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) Fuzzy TOPSIS 8 Review of topics 9 TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) Fuzzy TOPSIS (continued) 10 Fuzzy Classification Clustering analyses, Clustering validity 11 k-means method Fuzzy c-means method 12 Fuzzy pattern recognition 13 Decision Trees (ID3) 14 Fuzzy Decision Trees (Fuzzy ID3)

Recomended or Required Reading

Ana kaynaklar: Ross, T.J., Fuzzy Logic with Engineering Applications (4rd Edition), McGraw-Hill, 2016. Yardımcı kaynaklar: Lin, C.T. and George Lee, C.S., Neural Fuzzy Systems, Prentice Hall, 1996. Guanrong Chen, and Trung Tat Pham, Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems, 2019.

Planned Learning Activities and Teaching Methods

The course is taught in a lecture, class presentation and discussion format. In some weeks of the course, results of the homework given previously are discussed.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 VZ Vize 2 FN Final 3 BNS BNS VZ * 0.40 + FN * 0.60 4 BUT Bütünleme Notu 5 BBN Bütünleme Sonu Başarı Notu VZ * 0.40 + BUT * 0.60

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

ovgu.tekin@deu.edu.tr

Office Hours

Will be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 14 2 28 Tutorials 14 2 28 Preparations before/after weekly lectures 14 2 28 Preparation for midterm exam 1 15 15 Preparation for final exam 1 20 20 Final 1 2 2 Midterm 1 1 1 TOTAL WORKLOAD (hours) 122

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 PO.10 PO.11 PO.12 PO.13 LO.1 5 5 5 3 LO.2 5 5 5 3 LO.3 5 5 5 3 LO.4 5 5 5 3 LO.5 5 5 5 3