EEE 511 | Course Introduction and Application Information - M.SC. in Computer Engineering (Without Thesis)

Course Name

Artificial Neural Networks for Signal Processing and Control

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
EEE 511	Fall/Spring	3	0	3	7.5

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	Second Cycle
Mode of Delivery	-
Teaching Methods and Techniques of the Course	-
National Occupation Classification	-
Course Coordinator	Prof. Dr. Türker İNCE
Course Lecturer(s)	Prof. Dr. Türker İNCE
Assistant(s)	-

Course Objectives

The course aims the students: i) to know basic artificial neural networks models and learning algorithms and ii) to use artificial neural networks models and associated learning algorithms for signal processing and control applications.

Learning Outcomes

The students who succeeded in this course;

Classifyartificialneuralnetworksandalgorithms in terms of theirstructures, implementationandapplicationareas,
Choose suitable model andlearningalgorithmsfor a specificapplication.
Run learningalgorithmsefficiently in a numericalsoftwareenvinronment.
Use artificial neural networks and learning algorithms in signal processing and control applications.

Course Description

Artificial neural networks architectures and learning algorithms. Multi layer perceptron, radial basis function networks and support vector machines. Regression / function approximation, classification and clustering. Artificial neural networks for signal processing, filtering and pattern recognition. Artificial neural networks for system identification and control.

Course Category	Core Courses
	Major Area Courses
	Supportive Courses
	Media and Management Skills Courses
	Transferable Skill Courses

Week	Subjects	Related Preparation	Learning Outcome
1	Biological motivation. Historical remarks.	Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes.
2	Taxonomy of artificial neural networks and learning algorithms.	Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes.
3	Linear adaptive element, least mean square algorithm and convergence analysis.	Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes.
4	Discrete perceptron, perceptron learning rule and convergence analysis	Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes.
5	Multi-layer perceptron, back propagation algorithm and its variants with their convergence analyses, overfitting.	Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes.
6	Radial basis function networks, design by input and input-output clustering	Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes.
7	Support vector machines, Mercer theorem, kernel representation, Lagrange multipliers	Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes.
8	Generalization,Vapnik-Chervonenkis dimension.	Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes.
9	1. Midterm
10	Pattern recognition, feature extraction, dimension and data reduction by artificial neural networks.	Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes.
11	1-Dimensional biomedical signal processing by artificial neural networks.	Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes.
12	Biomedical image processing by artificial neural networks	Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes.
13	2. Midterm
14	Systems identification by artificial neural networks.	Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes.
15	Artificial neural networks based controller design.	Introduction. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761. Lecture Notes.,
16	Review of the Semester

Course Notes/Textbooks	The textbook referenced above and lecture notes
Suggested Readings/Materials	Related Books and Research Papers

Semester Activities	Number	Weigthing
Participation
Laboratory / Application	6	60
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury
Project	2	40
Seminar / Workshop
Oral Exams
Midterm
Final Exam
Total

Weighting of Semester Activities on the Final Grade	8	100
Weighting of End-of-Semester Activities on the Final Grade
Total

Semester Activities	Number	Duration (Hours)	Workload
Theoretical Course Hours (Including exam week: 16 x total hours)	16	3	48
Laboratory / Application Hours (Including exam week: '.16.' x total hours)	16	2	32
Study Hours Out of Class	15	4	60
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments			0
Presentation / Jury			0
Project	2	42	84
Seminar / Workshop			0
Oral Exam			0
Midterms			0
Final Exam			0
		Total	224

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	Program Competencies/Outcomes	1	2	3	4	5
1	Accesses information in breadth and depth by conducting scientific research in Computer Engineering, evaluates, interprets and applies information.		-	-	-	X	-
2	Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations.		-	-	-	-	X
3	Uses scientific methods to complete and apply information from uncertain, limited or incomplete data, can combine and use information from different disciplines.		-	-	-	X	-
4	Is informed about new and upcoming applications in the field and learns them whenever necessary.		-	-	-	-	X
5	Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions.		-	-	-	-	X
6	Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs.		-	-	-	-	X
7	Designs and implements studies based on theory, experiments and modelling, analyses and resolves the complex problems that arise in this process.		-	-	-	X	-
8	Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations, can work independently and takes responsibility.		-	-	-	-	X
9	Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.		-	-	X	-	-
10	Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.		-	-	X	-	-
11	Is knowledgeable about the social, environmental, health, security and law implications of Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer Engineering applications.		-	-	X	-	-
12	Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity.		-	X	-	-	-

EEE 511 | Course Introduction and Application Information

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

EVALUATION SYSTEM

ECTS / WORKLOAD TABLE

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

NEW GÜZELBAHÇE CAMPUS

GLOBAL CAREER

CONTRIBUTION TO SCIENCE

VALUING PEOPLE

BENEFIT TO SOCIETY