TYPE OF COURSE UNIT	Elective Course
LEVEL OF COURSE UNIT	Master's Degree With Thesis
YEAR OF STUDY	-
SEMESTER	-
NUMBER OF ECTS CREDITS ALLOCATED	10
NAME OF LECTURER(S)	Professor Hamit Erdem
LEARNING OUTCOMES OF THE COURSE UNIT	At the end of this course, the students; 1) Understand and explain strengths and weaknesses of the neural-network algorithms discussed in class. 2) Gain an ability to determine under which circumstances neural networks are useful in real applications. 3) Gain an ability to distinguish between supervised and unsupervised learning and explain the key principles of the corresponding algorithms. 4) Gain an ability to select appropriate neural network architectures for a given application. 5) Gain an ability to use MATLAB for programming ANN.
MODE OF DELIVERY	Face to face
PRE-REQUISITES OF THE COURSE	No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT	None
COURSE DEFINITION	General artificial neural network structures, feedforward and feedback networks, Hopfield, self organizing, radial basis function networks. Supervised and unsupervised learning, perceptron structures, backpropagation and other learning structures. Optimization methods, hierarchical networks and their applications.
COURSE CONTENTS	WEEK TOPICS 1st Week Introduction to artificial intelligence 2nd Week ANN and forward neural networks 3rd Week Learning algorithms and error based learning 4th Week Single layer perceptron, Adaline structure and LMS learning algorithm 5th Week Multilayer perceptron and back propagation learning algorithm 6th Week Multilayer perceptron and back propagation learning algorithm 7th Week RBF network 8th Week Midterm exam 9th Week Hopfield and unsupervised ANN 10th Week Hopfield and unsupervised ANN 11th Week SOM ANN 12th Week Project presentations 13th Week Project presentations 14th Week Project presentations
RECOMENDED OR REQUIRED READING	1. Neural Networks, Simon Haykin, 2nd. Edition. 2. Artificial Intelligence: A Guide to Intelligent Systems", Negnevitsky, Second edition.
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS	Presentation,Lecture
ASSESSMENT METHODS AND CRITERIA	Quantity Percentage(%) Mid-term 1 30 Assignment 1 15 Project 1 25 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 0 0 0 Individual or group work 14 8 112 Preparation for Final exam 1 40 40 Course hours 14 3 42 Preparation for Midterm exam 1 30 30 Laboratory (including preparation) 0 0 0 Final exam 1 3 3 Homework 2 35 70 Total Workload 300 Total Workload / 30 10 ECTS Credits of the Course 10
LANGUAGE OF INSTRUCTION	Turkish
WORK PLACEMENT(S)	No