TYPE OF COURSE UNIT	Elective Course
LEVEL OF COURSE UNIT	Doctorate Of Science
YEAR OF STUDY	-
SEMESTER	-
NUMBER OF ECTS CREDITS ALLOCATED	10
NAME OF LECTURER(S)	-
LEARNING OUTCOMES OF THE COURSE UNIT	At the end of this course, the students; 1) Understand what artificial neural network (ANN) means. 2) Comprehend the importance of ANNs for psychology and neuroscience. 3) Can discuss about how ANNs work. 4) Can discuss about ANN types. 5) Gain the opportunity to simulate learning with a simple neural network simulator.
MODE OF DELIVERY	Face to face
PRE-REQUISITES OF THE COURSE	No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT	None
COURSE DEFINITION	This course aims to teach about the artificial neural network and their usage in modelling of human behavior and cognitive processes. The course will involve the structure of artificial neural networks, connection geometry, network parameters, network types, threshold functions, learning paradigms and network learning algorithms, memory properties, generalization and computational power. Also, the course will cover topics such as perceptron models, connectionism and connectionist models and parallel distributed processing. By giving artificial neural networks built for visual perception, learning, language acquisition, speech perception and memory research, students will gain information about the place of artificial neural network in psychological research.
COURSE CONTENTS	WEEK TOPICS 1st Week Introduction 2nd Week Properties of Neurons 3rd Week Artificial Neurons and Neural Networks 4th Week Lateral Inhibition and Sensory Processing 5th Week ANN Types and Their History 6th Week Artificial Network Algorithms 7th Week Learning Algorithms 8th Week T-Learn Neural Network Simulation Program 9th Week Creating a Simple Neural Network with T-Learn 10th Week ANN Discussions 11th Week Representation of Knowledge 12th Week ANNs and Neuroscience 13th Week Hybrid Models 14th Week General Evaluation
RECOMENDED OR REQUIRED READING	Anderson, J.A., (1995). An Introduction to Neural Networks. MIT Press. Bar-Yam, Y. (1997). Dynamics of complex systems. Addison-Wesley. Rumelhart, D. & J. McClelland (1986). Parallel distributed processing. MIT Press, Cambridge, Mass * The basic materials to be used in this course are updated every year.
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS	Lecture,Discussion
ASSESSMENT METHODS AND CRITERIA	Quantity Percentage(%) Project 1 60 Total(%) 60 Contribution of In-term Studies to Overall Grade(%) 60 Contribution of Final Examination to Overall Grade(%) 40 Total(%) 100
ECTS WORKLOAD	Activities Number Hours Workload Midterm exam Preparation for Quiz Individual or group work 14 5 70 Preparation for Final exam 1 24 24 Course hours 14 3 42 Preparation for Midterm exam Laboratory (including preparation) Final exam 1 3 3 Homework 1 160 160 Total Workload 299 Total Workload / 30 9,96 ECTS Credits of the Course 10
LANGUAGE OF INSTRUCTION	Turkish
WORK PLACEMENT(S)	No