ARTIFICIAL NEURAL NETWORKS

PSK627

Course Code: PSK627
Semester: -
Theory+Practice: 3 + 0
ECTS: 10

Course Type

Elective Course

Course Level

Doctorate Of Science

Year of Study

Semester

ECTS

Name of Lecturer(s)

Learning Outcomes

At the end of this course, the students;

Understand what artificial neural network (ANN) means.
Comprehend the importance of ANNs for psychology and neuroscience.
Can discuss about how ANNs work.
Can discuss about ANN types.
Gain the opportunity to simulate learning with a simple neural network simulator.

Mode of Delivery

Face to face

Prerequisites

Recommended Optional Programme Components

None

Course Description

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
1^st Week	Introduction
2^nd Week	Properties of Neurons
3^rd Week	Artificial Neurons and Neural Networks
4^th Week	Lateral Inhibition and Sensory Processing
5^th Week	ANN Types and Their History
6^th Week	Artificial Network Algorithms
7^th Week	Learning Algorithms
8^th Week	T-Learn Neural Network Simulation Program
9^th Week	Creating a Simple Neural Network with T-Learn
10^th Week	ANN Discussions
11^th Week	Representation of Knowledge
12^th Week	ANNs and Neuroscience
13^th Week	Hybrid Models
14^th Week	General Evaluation

Recommended 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 Allocated Based on Student Workload

Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Midterm exam
Preparation for Quiz
Individual or group work	14	5	70
Preparation for Final exam	1	24	24
Course hours	15	3	45
Preparation for Midterm exam
Laboratory (including preparation)
Final exam	1	3	3
Homework	1	160	160
Total Work Load			302
Total Work Load / 30 (h)			10.06
ECTS Credit of the Course			10

Language of Instruction

Turkish

Work Placement(s)

Programme Qualifications / Learning Outcomes

	LO1	LO2	LO3	LO4	LO5
K1	X		X	X
K2					X
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K4	X	X		X
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	LO1	LO2	LO3	LO4	LO5
K1	X		X	X
K2					X
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K8
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	LO1	LO2	LO3	LO4	LO5
K1	X		X	X
K2					X
K3			X		X
K4	X	X		X
K5					X
K6	X		X
K7			X	X	X
K8
K9			X		X
K10			X		X
K11
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K13
K14					X
K15					X
K16		X
K17					X