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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS CSC 5053 FUZZY DATA ANALYSIS ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR EFENDI NASIBOĞLU

Offered to

Ph.D. in Computer Science (English) Industrial Ph.D. Program In Advanced Biomedical Technologies Computer Science Industrial Ph.D. Program In Advanced Biomedical Technologies Biomedical Tehnologies (English)

Course Objective

Data analysis techniques based on fuzzy sets theory which include uncertainty to the data and related methodologies will be the main focus of discussion in this course.

Learning Outcomes of the Course Unit

1   Have a good understanding of fuzzy data. 2   Have a good understanding of fuzzy classification. 3   Have a good understanding of fuzzy clustering. 4   Have a good understanding of fuzzy regression. 5   Have ability to make use of the tools for fuzzy data analysis.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Introduction 2 Data and uncertainty 3 Fuzzy logic and fuzzy sets 4 Fuzzy logic and fuzzy sets (Continues to ) 5 Fuzzy measurements 6 T-norm and T-conorm operations 7 Aggregation operators 8 Aggregation operators (cont) 9 Fuzzy classification 10 Fuzzy classification (Continues to ) 11 Fuzzy clustering 12 Fuzzy clustering (Continues to ) 13 Fuzzy regression 14 Model analysis

Recomended or Required Reading

H. Bandemer and W. Nather, 1992. Fuzzy Data Analysis, Kluwer Academic Publishers.

Planned Learning Activities and Teaching Methods

The course is taught in a lecture, class presentation and discussion format

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 PRJ PROJECT 2 FCG FINAL COURSE GRADE PRJ * 1 *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced

Language of Instruction

English

Course Policies and Rules

To be announced

Contact Details for the Lecturer(s)

efendi.nasiboglu@deu.edu.tr

Office Hours

To be announced

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 14 3 42 Preparation before/after weekly lectures 14 4 56 Preparing assignments 4 15 60 Preparation for Final Exam 1 20 20 Preparing presentations 4 5 20 Final 1 2 2 TOTAL WORKLOAD (hours) 200

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 LO.1 5 5 5 LO.2 5 5 5 LO.3 5 5 5 LO.4 5 5 5 LO.5 5 5 5