PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
ELK 522	ARTIFICIAL NEURAL NETWORKS	3 + 0	2nd Semester	7,5

COURSE DESCRIPTION

Course Level	Doctorate Degree
Course Type	Elective
Course Objective	To teach all types of artificial neural network structures and learning algorithms. To implement in the MatLab environment. To solve real world problems by using ANNs
Course Content	Basic neuron model and network architecture. Linear seperability. Perceptron learning rule. Performance surfaces and optimization. Supervised learning, error back-propagation and some variations. Momentum, variable learning rate. Gauss-Newton and Levenberg-Marquardt learning algorithms. Pattern recognition examples and Hopfield network.
Prerequisites	No the prerequisite of lesson.
Corequisite	No the corequisite of lesson.
Mode of Delivery	Face to Face

COURSE LEARNING OUTCOMES

1	Knows all types of artificial neural network structures
2	Knows ANN learning algorithms.
3	Can implement in the MatLab environment.
4	Can solve real world problems by using ANNs

COURSE'S CONTRIBUTION TO PROGRAM

	PO 01	PO 02	PO 03	PO 04	PO 05	PO 06	PO 07	PO 08	PO 09	PO 10	PO 11
LO 001	2	5	4	4	4
LO 002	2	5	4	4	4
LO 003	2	4	5	5	5
LO 004	2	4	5	5	5
Sub Total	8	18	18	18	18
Contribution	2	5	5	5	5	0	0	0	0	0	0

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION

Activities	Quantity	Duration (Hour)	Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)	14	3	42
Hours for off-the-classroom study (Pre-study, practice)	14	5	70
Mid-terms	1	40	40
Final examination	1	43	43
Total Work Load ECTS Credit of the Course			195 7,5

COURSE DETAILS

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L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes

Prospective Student