Software Engineering
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Ders Genel Tanıtım Bilgileri

Course Code:

FET306

Ders İsmi:

Applied Makes Neural Networks

Ders Yarıyılı:

Spring

Ders Kredileri:

Theoretical	Practical	Laboratory	ECTS
2	1	0	5

Language of instruction:

Turkish

Ders Koşulu:

Ders İş Deneyimini Gerektiriyor mu?:

Type of course:

Bölüm Seçmeli

Course Level:

Bachelor

TR-NQF-HE:6. Master`s Degree

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Assoc. Prof. Dr. CEVAT RAHEBİ

Course Lecturer(s):

Course Assistants:

Dersin Amaç ve İçeriği

Course Objectives:	To present the fundamental rules and techniques of neural network systems. To examine basic artificial neural network models and their applications.
Course Content:	Basic neural biology, neural network architectures and learning algorithms, artificial neural network applications, McCulloch-Pitts neurons, single-layer perceptron, multi-layer perceptron, radial basis function networks, Kohonen self-organizing maps, learning vector quantization.

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) To be able to describe the relationship between the brain and simple artificial neural network models.
2) To be able to explain the learning algorithms and the most common architectural structures for multi-layer perceptrons, radial basis function networks, and Kohonen self-organizing maps
3) To be able to distinguish between different neural network architectures, their limitations, and the appropriate learning rules for each architecture
4) To be able to identify the linear separability problem encountered in single-layer networks and to explain and demonstrate how this problem can be solved by adding a hidden layer.
5) To be able to discuss the main factors relevant to achieving good learning and generalization performance in artificial neural network systems
2 - Skills
Cognitive - Practical
3 - Competences
Communication and Social Competence
Learning Competence
Field Specific Competence
Competence to Work Independently and Take Responsibility

Ders Akış Planı

Week	Subject	Related Preparation
1)	Neural Networks and Their History, Biological Neurons, Artificial Neurons.
2)	Artificial Neural Networks, Single-Layer Perceptron, and Learning and Generalization in Single-Layer Perceptrons.
3)	Hebbian Learning, Gradient Descent Learnin
4)	The Generalized Delta Rule and Practical Considerations in Its Application
5)	Learning Process in Multi-Layer Perceptrons and the Backpropagation Algorith
6)	Momentum-based Learning and Conjugate Gradient Learning
7)	Bias-Variance Tradeoff, Underfitting and Overfitting Issues, and Strategies for Improving Generalization
8)	Midterm Exam
9)	Radial Basis Function Networks: Algorithms and Application
10)	Associative Learning
11)	Competitive Learning , Counterpropagation Networks , Grossberg Networks
12)	Adaptive Resonance Theory (ART), Stability
13)	Hopfield Networks , Bidirectional Associative Memories (BAMs)
14)	Self-Organizing Maps: Algorithms and Applications
15)	Final

Sources

Course Notes / Textbooks:
References:	1. Neural Networks: A Comprehensive Foundation, Simon Haykin, Pearson Education Inc. Leicestershire U.K 1999 2. Neural Networks for Pattern Recognition, C. Bishop, Oxford University Press, 1995 3. Principles of Neurocomputing for Science and Engineering, F.M.Ham and I.Kostanic, McGraw Hill, 2001

Ders - Program Öğrenme Kazanım İlişkisi

Ders Öğrenme Kazanımları	1	2	3	4	5
Program Outcomes
1) Adequate knowledge in mathematics and science; Ability to use theoretical and applied knowledge in these fields.
2) Sufficient knowledge of topics specific to the relevant engineering discipline; Ability to use theoretical and applied knowledge in these fields in solving complex engineering problems.
3) Ability to identify, formulate and solve complex engineering problems.
4) Ability to select and apply appropriate analysis and modeling methods in complex engineering problems.
5) The ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions.
6) Ability to apply modern design methods to design a complex system, process, device or product.
7) Ability to select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications.
8) Ability to use information technologies effectively to analyze and solve complex problems encountered in engineering applications.
9) Ability to design and conduct experiments for the study of complex engineering problems or discipline-specific research issues.
10) Ability to collect data, analyze and interpret results for the study of complex engineering problems or discipline-specific research topics.
11) Ability to work effectively in interdisciplinary teams.
12) Ability to work effectively in multidisciplinary teams.
13) Individual working ability.
14) Ability to communicate effectively verbally and in writing.
15) En az bir yabancı dil bilgisi.
16) Ability to write effective reports and understand written reports, and prepare design and production reports.
17) Ability to make effective presentations and give and receive clear and understandable instructions.
18) Awareness of the necessity of lifelong learning.
19) The ability to access information, follow developments in science and technology, and constantly renew oneself.
20) Knowledge of compliance with ethical principles, professional and ethical responsibility, and standards used in engineering practices.
21) Knowledge of business practices, such as project management, risk management and change management.
22) Awareness about entrepreneurship and innovation.
23) Information about sustainable development.
24) Information about the effects of engineering practices on health, environment and security at universal and social dimensions and the problems of the age reflected in the field of engineering.
25) Awareness of the legal consequences of engineering solutions.

Ders - Öğrenme Kazanımı İlişkisi

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Adequate knowledge in mathematics and science; Ability to use theoretical and applied knowledge in these fields.
2)	Sufficient knowledge of topics specific to the relevant engineering discipline; Ability to use theoretical and applied knowledge in these fields in solving complex engineering problems.
3)	Ability to identify, formulate and solve complex engineering problems.
4)	Ability to select and apply appropriate analysis and modeling methods in complex engineering problems.
5)	The ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions.
6)	Ability to apply modern design methods to design a complex system, process, device or product.
7)	Ability to select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications.
8)	Ability to use information technologies effectively to analyze and solve complex problems encountered in engineering applications.
9)	Ability to design and conduct experiments for the study of complex engineering problems or discipline-specific research issues.
10)	Ability to collect data, analyze and interpret results for the study of complex engineering problems or discipline-specific research topics.
11)	Ability to work effectively in interdisciplinary teams.
12)	Ability to work effectively in multidisciplinary teams.
13)	Individual working ability.
14)	Ability to communicate effectively verbally and in writing.
15)	En az bir yabancı dil bilgisi.
16)	Ability to write effective reports and understand written reports, and prepare design and production reports.
17)	Ability to make effective presentations and give and receive clear and understandable instructions.
18)	Awareness of the necessity of lifelong learning.
19)	The ability to access information, follow developments in science and technology, and constantly renew oneself.
20)	Knowledge of compliance with ethical principles, professional and ethical responsibility, and standards used in engineering practices.
21)	Knowledge of business practices, such as project management, risk management and change management.
22)	Awareness about entrepreneurship and innovation.
23)	Information about sustainable development.
24)	Information about the effects of engineering practices on health, environment and security at universal and social dimensions and the problems of the age reflected in the field of engineering.
25)	Awareness of the legal consequences of engineering solutions.

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Midterms	1	% 50
Final	1	% 50
total		% 100
PERCENTAGE OF SEMESTER WORK		% 50
PERCENTAGE OF FINAL WORK		% 50
total		% 100