EGE UNIVERSITY GRADUATE SCHOOL OF NATURAL AND APPLIED
SCIENCES COMPUTER ENGINEERING
DEPARTMENT 
20202021 SPRING SEMESTER 
Course 
618 DEEP LEARNING (3+0) 

Instructor 
Prof. Dr. Aybars UĞUR 

Course Place and Time 
Online (Teams and EGEDERS) (Tuesday, 13:1516:00) 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 (40%) · ML
Project · Presentation
1 · Presentation
2 · Quiz Final
Activities (60%) · Term
Project Report 1, Report 2, Report 3 · Term
Project, Paper, Video · Exam or
Quiz ? 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. 