Neural Networks and Applications (EE505) Course Detail

Course Name	Course Code	Season	Lecture Hours	Application Hours	Lab Hours	Credit	ECTS
Neural Networks and Applications	EE505	Area Elective	3	0	0	3	5

Pre-requisite Course(s)
N/A

Course Language	English
Course Type	Elective Courses
Course Level	Ph.D.
Mode of Delivery	Face To Face
Learning and Teaching Strategies	Lecture, Discussion, Question and Answer, Drill and Practice.
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
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 of topics
16	Final Examination period	Review of 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	Obtain ability to carry out advanced research activities, both individual and as a member of a team
2	Obtain ability to evaluate research topics and comment with scientific reasoning
3	Obtain ability to initiate and create new methodologies, implement them on novel research areas and topics
4	Obtain ability to produce experimental and/or analytical data in systematic manner, discuss and evaluate data to lead scintific conclusions
5	Obtain ability to apply scientific philosophy on analysis, modelling and design of engineering systems
6	Obtain ability to synthesis available knowledge on his/her domain to initiate, to carry, complete and present novel research at international level
7	Contribute scientific and technological advancements on engineering domain of his/her interest area
8	Contribute industrial and scientific advancements to improve the society through research activities

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