Information Package / Course Catalogue
Deep Learning
Course Code: EEE574
Course Type: Area Elective
Couse Group: Second Cycle (Master's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 2
Prt.: 2
Credit: 3
Lab: 0
ECTS: 8
Objectives of the Course

To teach students techniques based on Deep Learning and other learning methods and practical applications; Demonstrate the importance of deep learning in computer engineering, computer science and artificial intelligence.

Course Content

Artificial Neural Network Artificial Intelligence Machine Learning Single and Multi Layer Perceptrons

Name of Lecturer(s)
Assoc. Prof. Coşkun DENİZ
Learning Outcomes
1.To gain the ability to apply the basic concepts, techniques, mathematics and software infrastructure of artificial neural networks.
2.To recognize and use the ANN tools that are widely used today. To obtain the basic information necessary to create ANN libraries in new programming languages (such as Java, C #, python). To be able to develop projects in real life such as Estimation, Classification and Recognition.
3.To be able to develop intelligent software; to understand how machines can learn; To be able to make effective ANN designs.
4.Being able to follow the research topics developing in the field of Image Processing; To be able to make presentations by preparing short seminars on this subject.
5.To gain experience in reading and writing articles.
Recommended or Required Reading
1.Prof. Dr. Ercan Öztemel, 2003, “Yapay Sinir Ağları”, Papatya Yayıncılık, 238s. (Ders Kitabı).
2.Prof. Dr. Çetin Elmas, 2007, "Yapay Zeka Uygulamaları", Seçkin Yayıncılık, 425 s.
3.Haykin, Simon, 1998, “Neural Networks: A Comprehensive Fo-undation (2nd Edition)”, Prentice-Hall, 842p.
Weekly Detailed Course Contents
Week 1 - Theoretical & Practice
Acquaintance, Motivation and Importance of the course, Introduction to Artificial Neural Networks (ANN): Definition and Importance of ANN. ANN Tools and Software. Application Areas and Job Opportunities.
Week 2 - Theoretical & Practice
Artificial Intelligence (AI): What is Artificial Intelligence? Importance, Objectives, Subjects, Application Areas, Research Areas, AI Languages.
Week 3 - Theoretical & Practice
Fundamentals of ANN: Artificial Neuron and components. Types of Activation Functions. Biological Neuron. Biological Nervous System. Comparison of human brain and ANN.
Week 4 - Theoretical & Practice
Machine Learning.Supervised and Unsupervised Learning. Estimation, Classification and Clustering using ANN.
Week 5 - Theoretical & Practice
Single Layer Perceptrons: Examples of Perceptron ve ADALINE.
Week 6 - Theoretical & Practice
XOR Problem and need for Multi Layer Models.
Week 7 - Theoretical & Practice
Multi Layer Perceptrons (MLP).
Week 8 - Intermediate Exam
MidTerm Exam
Week 9 - Intermediate Exam
MidTerm Exam
Week 10 - Theoretical & Practice
Feed Forward Networks.
Week 11 - Theoretical & Practice
Back propagation Networks.
Week 12 - Theoretical & Practice
RBF (Radial Basis Function) Neural Networks.
Week 13 - Theoretical & Practice
LVQ (Learning Vector Quantization) Neural Networks.
Week 14 - Theoretical & Practice
SOM (Self-Orginizing Maps) Neural Networks.
Week 15 - Theoretical & Practice
Project presentation
Week 16 - Final Exam
Final Exam
Assessment Methods and Criteria
Type of AssessmentCountPercent
Assignment3%30
Term Assignment1%70
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory1664160
Assignment32521
Project1101020
TOTAL WORKLOAD (hours)201
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
OÇ-1
3
3
3
3
4
3
4
OÇ-2
4
4
4
3
5
4
3
OÇ-3
3
3
5
5
5
4
3
OÇ-4
3
3
3
3
4
3
4
OÇ-5
4
4
4
4
4
4
4
Adnan Menderes University - Information Package / Course Catalogue