EGE UNIVERSITY GRADUATE SCHOOL OF NATURAL AND APPLIED
SCIENCES COMPUTER ENGINEERING
DEPARTMENT 
20222023 SPRING SEMESTER 
Course 
618 DEEP LEARNING (3+0) 

Instructor 
Prof. Dr. Aybars UĞUR 

Course Place and Time 
Online (Teams and EGEDERS) (Tuesday, 13:3016:00) EGE University Computer Engineering Department 

Learning Outcomes 


Description 
Introduction to Deep Learning, Machine Learning Paradigms, Artificial Neural Networks, Ensemble Learning Methods, Convolutional Deep Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM), Deep Autoencoders, Other Deep Learning Methods, Hybrid Intelligent Systems. 

Syllabus 


Textbook 
·
Goodfellow, Y. Bengio and
A. Courville, “Deep
Learning”, MIT Press, 2016. ·
Ian Goodfellow, Yoshua
Bengio, Aaron Courville,
“Derin Öğrenme”, Buzdağı
Yayınları, 2018 (In Turkish) 

Reference Books 
·
Deep Learning with Python, François Chollet, 1st edition, Manning Publications. ·
Deniz KILINÇ, Nezahat BAŞEĞMEZ,
Uygulamalarla Veri Bilimi, Abaküs Yayın, 2018. ·
Michael Negnevitsky,
“Artificial Intelligence : A Guide to Intelligent Systems (3rd
Edition)”, Addison Wesley, 2011. Also Sample Papers will be given. 

Links 


Prerequisites 
Artificial Intelligence and Machine Learning Basics: Problem solving, state space search, machine learning principles, pattern recognition, fundamentals of computer vision. Basic knowledge of probability, statistics, calculus and linear algebra. Only for PhD Students. 

Grading 
Midterm Activities (60%) · DL / ML Project · Presentation 1 · Presentation 2 · Midterm Exam Final
Activities (40%) · Term Project Report 1, Report 2,
Report 3 · Term Project, Paper,
Video
For the
TakeHome and reports, students will be allowed a total of 5 (five) late
days; each additional late day will incur a 10% penalty. 