COURSE UNIT TITLE

: SOFT COMPUTING TECHNIQUES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
BIL 4109 SOFT COMPUTING TECHNIQUES ELECTIVE 3 0 0 5

Offered By

Computer Science

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASISTANT PROFESSOR METE EMINAĞAOĞLU

Offered to

Computer Science

Course Objective

This course aims to learn: introduction to soft computing, fundamentals of artificial neural network, fuzzy inference systems, genetic algorithm, simulated annealing, and hybrid systems.

Learning Outcomes of the Course Unit

1   Have a basic knowledge of artificial neural networks.
2   Have a basic knowledge of fuzzy inference systems.
3   Have a basic knowledge of evolutionary algorithms.
4   Have a basic knowledge of hybrid systems.
5   Have ability to construct models with soft computing.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Soft Computing Techniques
2 Simulated Annealing Algorithm
3 Examination of Simulated Annealing Algorithm Applications
4 Evalutionary Computation and Genetic Algorithm
5 Genetic Algorithm Operators
6 Examination of Genetic Algorithm Applications
7 Examination of Genetic Algorithm Applications (continue)
8 Midterm exam
9 Swarm Intelligence Algorithms and Ant Colony Algorithm
10 Ant Colony Algorithm
11 Examination of Ant Colony Algorithm Applications
12 Bee Colony Based Algorithms and Artificial Bee Colony Algorithm
13 Artificial Bee Colony Algorithm
14 Examination of Artificial Bee Colony Algorithm Applications

Recomended or Required Reading

Textbook(s):
Neural Network, Fuzzy Logic, and Genetic Algorithms - Synthesis and Applications", by S. Rajasekaran and G.A. Vijayalaksmi Pai, (2005), Prentice Hall,
Soft Computing and Intelligent Systems - Theory and Application , by Naresh K. Sinha and Madan M. Gupta (2000), Academic Press,
Supplementary Book(s):
Soft Computing and Intelligent Systems Design - Theory, Tools and Applications", by Fakhreddine karray and Clarence de Silva (2004), Addison Wesley
Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence by J. S. R. Jang, C. T. Sun, and E. Mizutani, (1996), Prentice Hall.

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 ASG ASSIGNMENT
2 MTE MIDTERM EXAM
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE ASG * 0.45 + MTE * 0.25 + FIN * 0.30
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) ASG * 0.45 + MTE * 0.25 + RST * 0.30


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.nasibov@deu.edu.tr
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 13 3 39
Preparations before/after weekly lectures 12 3 36
Preparation for midterm exam 1 12 12
Preparation for final exam 1 18 18
Preparing assignments 4 6 24
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 133

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13
LO.1545355455
LO.255525455
LO.3554355
LO.455332255
LO.54554345