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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS CSC 5001 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 EFENDI NASIBOĞLU

Offered to

Computer Science Ph.D. in Computer Science

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 Midterm exam 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 MTE MIDTERM EXAM 2 ASG ASSIGNMENT 3 FIN FINAL EXAM 4 FCG FINAL COURSE GRADE MTE * 0.30 +ASG * 0.20 +FIN * 0.50 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + ASG * 0.20 + RST * 0.50

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)

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 13 3 39 Preparation before/after weekly lectures 13 4 52 Preparation for Mid-term Exam 1 20 20 Preparation for Final Exam 1 30 30 Preparing Individual Assignments 6 8 48 Final 1 2 2 Mid-term 1 2 2 TOTAL WORKLOAD (hours) 193

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