Akademik Dersler - Tanıtım


Code - Course Title	BİL624 - Deep Learning Theory and Applications
Course Type	Elective Courses
Language of Instruction	Türkçe
Laboratory + Practice	3+0
ECTS	7.5
Course Instructor(s)	DOKTOR ÖĞRETİM ÜYESİ CAHİT PERKGÖZ
Mode of Delivery	Face to face.
Prerequisites	None.
Courses Recomended	Some basics of Linear Algebra, Probability and Artificial Intelligence Courses.
Recommended Reading List	M. Nielsen, “Neural Networks and Deep Learning”, http://neuralnetworksanddeeplearning.com
Assessment methods and criteria	2 Midterm exams, 1 Final exam, Homework assignments.
Work Placement	Not Applicable
Sustainability Development Goals

Weeks	Topics
Week - 1	Introduction; Related Linear Algebra and Probability
Week - 2	Deep Feed Forward Neural Networks
Week - 3	Eigen Value Decomposition, Principal Component Analysis, Singular Value Decomposition
Week - 4	Deep Autoencoders
Week - 5	Regularization: Early stopping, Noise injection, Ensemble methods
Week - 6	Deep Convolutional Neural Networks
Week - 7	Deep Reinforcement Learning
Week - 8	Sequence Learning Problems, Deep Recurrent Neural Networks
Week - 9	Long Short Term Memory Cells, Gated Recurrent Units
Week - 10	Attention Mechanism
Week - 11	Directed Graphical Methods
Week - 12	Generative Adversarial Networks
Week - 13	Deep Learning Applications
Week - 14	Project Presentations

Assessment Method and Passing Requirements
	Quamtity	Percentage (%)
Toplam (%)		0