Neural Networks and Applications (EE423) Course Detail

Course Name	Course Code	Season	Lecture Hours	Application Hours	Lab Hours	Credit	ECTS
Neural Networks and Applications	EE423	Area Elective	3	0	0	3	5

Pre-requisite Course(s)
N/A

Course Language	English
Course Type	Elective Courses
Course Level	Bachelor’s Degree (First Cycle)
Mode of Delivery	Face To Face
Learning and Teaching Strategies	Lecture, Demonstration, Discussion, Question and Answer, Drill and Practice, Team/Group, Brain Storming.
Course Coordinator
Course Lecturer(s)	Asst. Prof. Dr. Hakan Tora
Course Assistants
Course Objectives	•Introduce the main fundamental principles and techniques of neural network systems. •Investigate the principal neural network models and applications.
Course Learning Outcomes	The students who succeeded in this course; Ability to describe the relation between real brains and simple artificial neural network models Ability to explain and contrast the most common architectures and learning algorithms for Multi-Layer Perceptrons, Radial-Basis Function Networks, Committee Machines, and Kohonen Self-Organizing Maps Ability to identify different neural network architectures, their limitations and appropriate learning rules for each of the architectures Ability to verify the classic linear separability problem that exists for single layer networks, demonstrate and explain how adding a hidden layer solves the problem Ability to discuss the main factors involved in achieving good learning and generalization performance in neural network systems Ability to design and implement neural network systems to solve real-world problems (classification, pattern recognition)
Course Content	An introduction to basic neurobiology, the main neural network architectures and learning algorithms, and a number of neural network applications, McCulloch Pitts Neurons, Single Layer Perceptrons, Multi-Layer Perceptrons, Radial Basis Function Networks, Committee Machines, Kohonen Self-Organising Maps, and Learning Vector Quantization

Weekly Subjects and Releated Preparation Studies

Week	Subjects	Preparation
1	Introduction to Neural Networks and their History. Biological Neurons and Neural Networks. Artificial Neurons	Glance this week’s topics from the lecture
2	Networks of Artificial Neurons. Single Layer Perceptrons. Learning and Generalization in Single Layer Perceptrons	Glance this week’s topics from the lecture
3	Hebbian Learning. Gradient Descent Learning	Glance this week’s topics from the lecture
4	The Generalized Delta Rule. Practical Considerations	Glance this week’s topics from the lecture
5	Learning in Multi-Layer Perceptrons. Back-Propagation Algorithms	Glance this week’s topics from the lecture
6	Learning with Momentum. Conjugate Gradient Learning	Review last week and glance this week’s topics from the lecture
7	Bias and Variance. Under-Fitting and Over-Fitting. Improving Generalization	Review last week and glance this week’s topics from the lecture
8	Applications of Multi-Layer Perceptrons	Glance this week’s topics from the lecture
9	Radial Basis Function Networks: Introduction, Algorithms, and Applications	Glance this week’s topics from the lecture
10	Associative learning	Glance this week’s topics from the lecture
11	Competitive networks, Counterpropagation networks, Grossberg networks	Glance this week’s topics from the lecture
12	Adaptive resonance theory, stability	Glance this week’s topics from the lecture
13	Hopfield networks, bidirectional associative memories	Glance this week’s topics from the lecture
14	Self Organizing Maps: Fundamentals, Algorithms, and Applications	Glance this week’s topics from the lecture
15	Final examination period	Review topics
16	Fimal examination period	Review topics

Sources

Course Book	1. Neural Networks: A Comprehensive Foundation, Simon Haykin, Pearson Education Inc. Leicestershire U.K 1999
Other Sources	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

Evaluation System

Requirements	Number	Percentage of Grade
Attendance/Participation	-	-
Laboratory	-	-
Application	-	-
Field Work	-	-
Special Course Internship	-	-
Quizzes/Studio Critics	-	-
Homework Assignments	15	20
Presentation	-	-
Project	1	20
Report	-	-
Seminar	-	-
Midterms Exams/Midterms Jury	2	30
Final Exam/Final Jury	1	30
Toplam	19	100

Percentage of Semester Work	70
Percentage of Final Work	30
Total	100

Course Category

Core Courses	X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

#	Program Qualifications / Competencies	Level of Contribution
#	Program Qualifications / Competencies	1	2	3	4	5
1	Adequate knowledge of subjects related to mathematics, natural sciences, and Electrical and Electronics Engineering discipline; ability to apply theoretical and applied knowledge in those fields to the solution of complex engineering problems.					X
2	An ability to identify, formulate, and solve complex engineering problems, ability to choose and apply appropriate models and analysis methods for this.					X
3	An ability to design a system, component, or process under realistic constraints to meet desired needs, and ability to apply modern design approaches for this.			X
4	The ability to select and use the necessary modern techniques and tools for the analysis and solution of complex problems encountered in engineering applications; the ability to use information technologies effectively		X
5	Ability to design and conduct experiments, collect data, analyze and interpret results for investigating complex engineering problems or discipline-specific research topics.					X
6	An ability to function on multi-disciplinary teams, and ability of individual working.			X
7	Ability to communicate effectively orally and in writing; knowledge of at least one foreign language; active report writing and understanding written reports, preparing design and production reports, the ability to make effective presentation the ability to give and receive clear and understandable instructions.					X
8	Awareness of the necessity of lifelong learning; the ability to access knowledge, follow the developments in science and technology and continuously stay updated.			X
9	Acting compliant with ethical principles, professional and ethical responsibility, and knowledge of standards used in engineering applications.			X
10	Knowledge about professional activities in business, such as project management, risk management, and change management awareness of entrepreneurship and innovation; knowledge about sustainable development.				X
11	Knowledge about the impacts of engineering practices in universal and societal dimensions on health, environment, and safety. the problems of the current age reflected in the field of engineering; awareness of the legal consequences of engineering solutions.					X

ECTS/Workload Table

Activities	Number	Duration (Hours)	Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)	16	3	48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class	16	2	32
Presentation/Seminar Prepration
Project	4	5	20
Report
Homework Assignments	8	2	16
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury	2	3	6
Prepration of Final Exams/Final Jury	1	3	3
Total Workload			125