Information Package / Course Catalogue
Artificial Neural Networks Methods
Course Code: BİS525
Course Type: Area Elective
Couse Group: Second Cycle (Master's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 8
Objectives of the Course

Students will make familiar with the basics of artificial neural networks methods and with their applicability to various problems including time series, regression, clustering, classification and dimension reduction.

Course Content

Theory and application of regression, classification, time series and data reduction.

Name of Lecturer(s)
Prof. İmran KURT ÖMÜRLÜ
Learning Outcomes
1.To be able to define basic neural network models
2.To be able to use commonly used ANN models and learning algorithms for a specific application
3.To learn the principles of generalization ability with supervized and unsupervized learning
4.To be able to learn the advantages and limitations of ANN
5.To be able to make applications about classification and regression problems by using artificial neural networks
Recommended or Required Reading
Weekly Detailed Course Contents
Week 1 - Theoretical
Real and artificial nerve cells
Week 2 - Theoretical
History of artificial neural networks
Week 3 - Theoretical
Purposes of use of artificial neural networks
Week 4 - Theoretical
Structure and basic elements of artificial neural networks
Week 5 - Theoretical
Examination of activation functions
Week 6 - Theoretical
Perceptrons
Week 7 - Theoretical
Multi-layer perceptrons
Week 8 - Intermediate Exam
Midterm exam
Week 9 - Theoretical
Forward and feedback networks
Week 10 - Theoretical
Artificial neural networks according to learning algorithms
Week 11 - Theoretical
Training of artificial neural networks and performance measures
Week 12 - Theoretical
Adaptive resonance theory (Art) networks
Week 13 - Theoretical
Recycled networks (Elman Network) and other artificial neural network models
Week 14 - Theoretical
Prediction, classification and clustering with ANN.
Week 15 - Theoretical
Literature review and discussion
Week 16 - Final Exam
Final exam
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%40
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140342
Assignment110010
Seminar115217
Reading54125
Individual Work100220
Quiz142142
Midterm Examination120222
Final Examination120222
TOTAL WORKLOAD (hours)200
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
OÇ-1
3
5
4
4
5
4
3
4
5
4
OÇ-2
OÇ-3
OÇ-4
3
4
4
5
5
5
5
4
5
4
OÇ-5
Adnan Menderes University - Information Package / Course Catalogue