COURSE UNIT TITLE

: ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHMS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
EKO 6067 ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHMS ELECTIVE 3 0 0 6

Offered By

Econometrics

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

PROFESSOR DOCTOR IPEK DEVECI KOCAKOÇ

Offered to

Econometrics

Course Objective

The main objective of the course is to give actual informations about Artificial Neural Networks And Genetic Algorithms to students,to be able to follow new developments about this subject and to develop using this method when encounter problems.

Learning Outcomes of the Course Unit

1   To be able to understand the basic principle of Artificial Neural Networks techniques.
2   To be able to understand the basic principle of Genetic Algorithms techniques
3   To be able to use the techniques as an instrument for operation research.
4   To be able to understand the diffirences of Artificial Neural Networks And Genetic Algorithms from the other classical methods and to understand when they are necessary .
5   To be able to produce active solutions about specific business problems in life.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Binary Genetic Algorithms
2 Selecting Parameters of Genetic Algorithm
3 Genetic Algorithm with Constant Parameters
4 Alternative Crossover and Mutation Techniques
5 Analysis of Genetic Algorithms with Matlab
6 The Actual Structure of Neural Networks
7 Artificial Neural Networks and Their Properties
8 Mid-term
9 Artificial Neural Networks in Social Sciences
10 Introduction to Back Spreading algorithm
11 Continuation Back Spreading algorithm
12 Decision Support Vector
13 Analysis of Neural Networks with Matlab
14 Analysis of Neural Networks with Matlab

Recomended or Required Reading

David E. Goldberg.Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley. 1989.
Randy L.Haupt and Sue Ellen Haupt.Practical Genetic Algorithms. Wiley-Blackwell.2004.
Jeff Heaton. Introduction to the Math of Neural Networks Heaton Research.2012.
G David Garson. Neural Networks: An Introductory Guide for Social Scientists Sage Publications.1998.

Planned Learning Activities and Teaching Methods

This course will be presented using methods of expression, discussion and solving problem.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 STT TERM WORK (SEMESTER)
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.30 + STT * 0.30 + FIN* 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + STT * 0.30 + RST* 0.40


*** 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

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

To 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 13 3 39
Preparation for midterm exam 1 30 30
Preparation for final exam 1 30 30
Midterm 1 3 3
Final 1 3 3
TOTAL WORKLOAD (hours) 144

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9
LO.11
LO.21
LO.31
LO.41
LO.51

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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