COURSE UNIT TITLE

: SOFT COMPUTING TECHNOLOGIES

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
STA 6016 SOFT COMPUTING TECHNOLOGIES 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
Statistics (English)
Biomedical Tehnologies (English)
STATISTICS (ENGLISH)
Statistics (English)

Course Objective

The objective of the course is to introduce basics of Soft Computing (SC) technologies which has gains popularity in data analysis and decision making problems for recent years.

Learning Outcomes of the Course Unit

1   Have a good understanding of basic SC concepts.
2   Have a good understanding about the SC techniques.
3   Have a basic knowledge of current SC applications.
4   Have ability to use a basic SC software.
5   Have ability to design SC applications.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 INTRODUCTION TO FUZZY AND SOFT COMPUTING
2 FUZZY SETS
3 FUZZY RULES AND FUZZY REASONING
4 FUZZY INFERENCE SYSTEMS, ASSIGNMENT 1
5 LEAST-SQUARES METHODS FOR SYSTEM IDENTIFICATION
6 DERIVATIVE-BASED OPTIMIZATION, ASSIGNMENT 2
7 DERIVATIVE-FREE OPTIMIZATION
8 DERIVATIVE-FREE OPTIMIZATION (cont)
9 NEURAL NETWORKS
10 ANFIS: ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS
11 CLASSIFICATION AND REGRESSION TREES
12 DATA CLUSTERING ALGORITHMS, ASSIGMENT 3
13 RULEBASE STRUCTURE IDENTIFICATION
14 SOFT COMPUTING APPLICATIONS

Recomended or Required Reading

Textbook(s)/References/Materials:
J.-S.R. Jang, C.-T. Sun, E. Mizutani, Neuro-Fuzzy And Soft Computing, by Prentice Hall, 1997.
Supplementary Book(s):
R.A. Aliev, R.R. Aliev, Soft Computing and Its Applications, by World Scientific Publishing Co., 2001.

Planned Learning Activities and Teaching Methods

The course is taught in a lecture, class presentation and discussion format. Besides the taught lecture, group presentations are to be prepared by the groups assigned and presented in a discussion session. In some weeks of the course, results of the homework given previously are discussed.

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

Evaluation of exams and homeworks.

Language of Instruction

English

Course Policies and Rules

To be announced

Contact Details for the Lecturer(s)

efendi.nasibov@deu.edu.tr

Office Hours

Will be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 4 56
Preparation before/after weekly lectures 14 4 56
Preparing Individual Assignments 3 12 36
Preparation for Mid-term Exam 1 26 26
Preparation for Final Exam 1 30 30
Final 1 2 2
Mid-term 1 2 2
TOTAL WORKLOAD (hours) 208

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.15555
LO.25555
LO.35555
LO.45555
LO.55555

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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