Akademik Dersler - Tanıtım


Code - Course Title	EEM624 - Advanced Topics in Deep Learning
Course Type	Elective Courses
Language of Instruction	İngilizce
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.
Required or Recommended Resources	I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, MIT Press, 2016.
Recommended Reading List	M. Nielsen, “Neural Networks and Deep Learning”, http://neuralnetworksanddeeplearning.com
Assessment methods and criteria	2 Midterm exams, 1 Final exam, 1 Homework assignment and 4 Quizzes.
Work Placement	Not Applicable
Sustainability Development Goals

Weeks	Topics
Week - 1	1. Introduction; Related Linear Algebra, Probability and Optimization Review
Week - 2	2. Machine Learning Basics
Week - 3	Deep Feedforward Networks
Week - 4	Regularization and Optimization for Deep Models
Week - 5	Deep Convolutional Neural Networks
Week - 6	Representation Learning
Week - 7	Recurrent Neural Networks
Week - 8	Deep Reinforcement Learning
Week - 9	Linear Factor Models
Week - 10	Autoencoders
Week - 11	Generative Adversarial Networks
Week - 12	Finite Markov Decision Processes
Week - 13	Monte Carlo Methods
Week - 14	Deep Generative Models

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