ARTIFICIAL NEURAL NETWORKS PROGRAMME COURSE DESCRIPTION

Code	Name of the Course Unit	Semester	In-Class Hours (T+P)	Credit	ECTS Credit
BIL422	ARTIFICIAL NEURAL NETWORKS	5	3	3	6

GENERAL INFORMATION
Language of Instruction :	Turkish
Level of the Course Unit :	BACHELOR'S DEGREE, TYY: + 6.Level, EQF-LLL: 6.Level, QF-EHEA: First Cycle
Type of the Course :	Elective
Mode of Delivery of the Course Unit	-
Coordinator of the Course Unit	Assist.Prof. OĞUZHAN ÖZTAŞ
Instructor(s) of the Course Unit	Lecturer KÜBRA ERDOĞAN
Course Prerequisite	No

OBJECTIVES AND CONTENTS
Objectives of the Course Unit:	The aim of this course is to provide comprehensive knowledge on the design, training, and testing of artificial neural networks.
Contents of the Course Unit:	This course covers biological neurons and the brain, the model of a single neuron, neural networks, training algorithms, applications, benefits, weaknesses, and various uses of neural networks.

KEY LEARNING OUTCOMES OF THE COURSE UNIT (On successful completion of this course unit, students/learners will or will be able to)
Recognize and solve problems that can be addressed using artificial neural network algorithms.

WEEKLY COURSE CONTENTS AND STUDY MATERIALS FOR PRELIMINARY & FURTHER STUDY
Week	Preparatory	Topics(Subjects)	Method
1	-	Overview of Artificial Intelligence and Machine Learning	-
2	-	Introduction to Artificial Neural Networks	-
3	-	Structure and Basic Elements of Artificial Neural Networks	-
4	-	Early Artificial Neural Networks	-
5	-	Artificial Neural Network Model (Supervised Learning) Multilayer Perceptron	-
6	-	Artificial Neural Network Model (Supportive Learning) LVQ Model	-
7	-	Artificial Neural Network Model (Unsupervised Learning) Adaptive Resonance Theory (ART) Networks	-
8	-	MID-TERM EXAM	-
9	-	Recurrent Networks (Element Networks) and Other Artificial Neural Network Models	-
10	-	Hybrid Artificial Neural Networks	-
11	-	Hardware for Artificial Neural Networks	-
12	-	Overview of Applications of Artificial Neural Networks	-
13	-	Student Project Presentations	-
14	-	Student Project Presentations	-
15	-	ANN: General Overview	-
16	-	FINAL EXAM	-
17	-	FINAL EXAM	-

SOURCE MATERIALS & RECOMMENDED READING
Laurene V. Fausett, “Fundamentals of Neural Networks: Architectures, Algorithms And Applications”, Prentice Hall.
Simon Haykin, “Neural Networks: A Comprehensive Foundation”, Prentice Hall
Paul E. Keller, Kevin L. Priddy, "Artificial Neural Networks: an Introduction", PHI, 2007
Ercan Öztemel, “Yapay Sinir Ağları”, Papatya Yayıncılık, 2012
Vasif Nabiyev, "Yapay Zeka", Seçkin Yayınları, 3. baskı 2010

ASSESSMENT
Assessment & Grading of In-Term Activities	Number of Activities	Degree of Contribution (%)	Description

Level of Contribution
0	1	2	3	4	5

KNOWLEDGE
Theoretical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Explains the fundamental engineering concepts of computer science and relates them to the groundwork of computer science.						5

KNOWLEDGE
Factual
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Uses theoretical and practical knowledge coming from mathematics, probability, statistics and various other branches of life sciences, to find solutions to engineering problems.					4

SKILLS
Cognitive
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Determines the components and the underlying process of a system and designs an appropriate computational model under reasonable constraints.					4
2	Designs a computer-aided conceptual model with modern techniques.					4

SKILLS
Practical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Determines, detects and analyzes the areas of computer science applications and develops appropriate solutions.					4
2	Identifies, models and solves computer engineering problems by applying appropriate analytical methods.					4
3	Determines and uses the necessary information technologies in an efficient way for engineering applications.				3

OCCUPATIONAL
Autonomy & Responsibility
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Possess the responsibility and ability to design and conduct experiments for engineering problems by collecting, analyzing and interpreting data.					4
2	Possess the ability to conduct effective individual study.				3
3	Takes responsibility as a team work and contributes in an effective way.			2

OCCUPATIONAL
Learning to Learn
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Monitors the developments in the field of information technologies by means of internet and related journals and possess the required knowledge for the management, control, development and security of information technologies.					4
2	Develops positive attitude towards lifelong learning.				3

OCCUPATIONAL
Communication & Social
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Communicates effectively by oral and/or written form and uses at least one foreign language.			2
2	Possess sufficient consciousness about the issues of project management, practical applications and also environmental protection, worker's health and security.				3

OCCUPATIONAL
Occupational and/or Vocational
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Possess professional and ethical responsibility and willingness to share it.			2
2	Possess sufficient consciousness about the universality of engineering solutions and applications and be well aware of the importance of innovation.					4

WORKLOAD & ECTS CREDITS OF THE COURSE UNIT
Workload for Learning & Teaching Activities
Type of the Learning Activites	Learning Activities (# of week)	Duration (hours, h)	Workload (h)
Lecture & In-Class Activities	14	3	42
Preliminary & Further Study	14	3	42
Land Surveying	0	0	0
Group Work	0	0	0
Laboratory	0	0	0
Reading	0	0	0
Assignment (Homework)	0	0	0
Project Work	0	0	0
Seminar	0	0	0
Internship	0	0	0
Technical Visit	0	0	0
Web Based Learning	0	0	0
Implementation/Application/Practice	0	0	0
Practice at a workplace	0	0	0
Occupational Activity	0	0	0
Social Activity	0	0	0
Thesis Work	0	0	0
Field Study	0	0	0
Report Writing	0	0	0
Final Exam	1	2	2
Preparation for the Final Exam	1	35	35
Mid-Term Exam	1	2	2
Preparation for the Mid-Term Exam	1	30	30
Short Exam	0	0	0
Preparation for the Short Exam	0	0	0
TOTAL	32	0	153