Akademik Dersler - Tanıtım


Code - Course Title	BİM447 - Introduction to Deep Learning
Course Type	Area Elective Courses
Language of Instruction	İngilizce
Laboratory + Practice	3+0
ECTS	4.5
Course Instructor(s)	DOKTOR ÖĞRETİM ÜYESİ CAHİT PERKGÖZ
Mode of Delivery	Face to face.
Prerequisites	None.
Courses Recomended	None.
Recommended Reading List	http://neuralnetworksanddeeplearning.com/
Assessment methods and criteria	1 Midterm Exam, 1 Final Exam, Homeworks, Project.
Work Placement	None.
Sustainability Development Goals

Weeks	Topics
Week - 1	Introduction
Week - 2	Mathematics for Deep Learning
Week - 3	Machine Learning Basics
Week - 4	Computational Graphs
Week - 5	Deep Neural Networks: Multi Layer Perceptrons
Week - 6	Deep Neural Networks: Loss Function, Activation Function, Preprocessing
Week - 7	Overfitting and Regularization
Week - 8	Optimization
Week - 9	Deep Convolutional Neural Networks
Week - 10	Sequence Models
Week - 11	Generative Adversarial Networks
Week - 12	Autoencoders
Week - 13	Deep Learning Applications
Week - 14	Project Presentations

Assessment Method and Passing Requirements
	Quamtity	Percentage (%)
1.Midterm Exam	1	30
Project	1	30
Final Exam	1	40
Toplam (%)		100