PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
YOBS 543	ARTIFICIAL NEURAL NETWORKS	3 + 0	1st Semester	6

COURSE DESCRIPTION

Course Level	Master's Degree
Course Type	Elective
Course Objective	The aim of the course is to give students a basic theoretical knowledge of artificial neural networks and deep learning and how to practically use them for typical problem solving processes
Course Content	1. The emergence of artificial neural networks(ANNs) 2. Fundamental concepts of (ANNs) 3. ANN structures 4. Learning algorithms (supervised,unsupervised) 5. Training Single Layer ANNs 6. Training Multi Layer ANNs 7. Feed-forward neural networks 8. Recurrent neural networks 9. Deep learning
Prerequisites	No the prerequisite of lesson.
Corequisite	No the corequisite of lesson.
Mode of Delivery	Face to Face

COURSE LEARNING OUTCOMES

1	Can describe the ANN structures
2	Knows the construction of single layer perceptron
3	be able toconstruct multi-layer perceptron
4	be able to describe the construction of different types of deep neural networks
5	be able to analyse a typical problem within the subject area and deduce which method or methods that are most suitable to solve it

COURSE'S CONTRIBUTION TO PROGRAM

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	3	42
Assignments	1	41	41
Mid-terms	1	13	13
Final examination	1	18	18
Total Work Load ECTS Credit of the Course			156 6

COURSE DETAILS

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

Prospective Student