Course Information

Course Name	Turkish	Yapay Sinir Ağları
	English	Artificial Neural Networks
Course Code	EHB 420E	Credit	Lecture (hour/week)	Recitation (hour/week)	Laboratory (hour/week)
Semester	2	3	3	-	-
Course Language		English
Course Coordinator		Ömer Melih Gül
Course Objectives		1. To provide an understanding of artificial neural networks (ANN) 2. To provide an understanding of how ANN are applied to the real world engineering problems.
Course Description		1. Biological neural systems 2. Introduction to artificial neural networks 3. Linear models for regression and classification 4. Supervised, unsupervised, and self-supervised learning 5. ANNs architectures. Perceptron learning rule. Hebbian learning rule 6. Optimization methods. Gradient descent learning rule 7. Single layer neural networks 8. Multi-layered perceptron design. Back propagation algorithm 9. Radial basis function networks 10. Kohonen’s self-organizing maps 11. ANNs applications: Deep learning, engineering applications, etc.
Course Outcomes		1. Understand the fundamentals of artificial neural networks (ANNs). 2. Gain some mathematical understanding of neural network models. 3. Gain abilities to select suitable neural network models. 4. Learn how to use ANNs for problems of classification and regression. 5. Evaluate the performance of a neural network and tune the neural network to get the best performance. 6. Having ability to apply the concepts of ANNs to real-world engineering problems.
Pre-requisite(s)
Required Facilities
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Textbook		Neural Networks for Pattern Recognition, Christopher Bishop, Clarendon Press, Oxford, 1995. Neural Networks: A Comprehensive Foundation, 2nd Ed., Simon Haykin, Prentice Hall, 1999. Neural Networks and Learning Machines, Simon Haykin, Pearson Higher Education, 2009.
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