TYPE OF COURSE UNIT	Elective Course
LEVEL OF COURSE UNIT	Master's Degree With Thesis
YEAR OF STUDY	-
SEMESTER	-
NUMBER OF ECTS CREDITS ALLOCATED	7
NAME OF LECTURER(S)	-
LEARNING OUTCOMES OF THE COURSE UNIT	At the end of this course, the students; 1) Understand what an artificial neural network (ANN) is. 2) Understand why ANN's are crucial for psychology and neuroscience. 3) Gain knowledge about how ANN's work. 4) Gain knowledge about different types of ANN's. 5) Find opportunity to simulate a learning system with an easy neural net simulator.
MODE OF DELIVERY	Face to face
PRE-REQUISITES OF THE COURSE	No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT	None
COURSE DEFINITION	This course introduces students to artificial neural networks and the use of artificial neural networks in the modeling of human behavior and cognition. Topics covered in the course include: the structure and components of artificial neural networks, connection geometry, network parameters, network types, threshold functions, learning paradigms and algorithms, memory properties, generalization and computational power of artificial neural networks. The course also examines the perceptron model, connectionism and connectionist models, and parallel distributed processing. Drawing on sample network models of visual perception, learning, speech perception, language acquisition, and learning, the course aims to inform students of the use of artificial neural networks in psychological research.
COURSE CONTENTS	WEEK TOPICS 1st Week Introduction 2nd Week Neurons 3rd Week Artificial Neurons and Artificial Neural Networks 4th Week Lateral Inhibition and Sensory Processing 5th Week Types and History of ANN 6th Week Artificial Network Algorithms 7th Week Learning Algorithms 8th Week T-learn Neural Network Simulation Program 9th Week Constructing a simple ANN with T-learn 10th Week Criticisms of ANN 11th Week Representation of Information 12th Week ANN's and Neuroscience 13th Week Hybrid Models 14th Week Integration and Review Session
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.
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS	Lecture,Discussion
ASSESSMENT METHODS AND CRITERIA	Quantity Percentage(%) Mid-term 1 30 Project 1 40 Total(%) 70 Contribution of In-term Studies to Overall Grade(%) 70 Contribution of Final Examination to Overall Grade(%) 30 Total(%) 100
ECTS WORKLOAD	Activities Number Hours Workload Midterm exam 1 3 3 Preparation for Quiz Individual or group work 14 8 112 Preparation for Final exam 1 20 20 Course hours 14 3 42 Preparation for Midterm exam 1 15 15 Laboratory (including preparation) Final exam 1 3 3 Homework Project 1 10 10 Total Workload 205 Total Workload / 30 6,83 ECTS Credits of the Course 7
LANGUAGE OF INSTRUCTION	Turkish
WORK PLACEMENT(S)	No