EGE UNIVERSITY GRADUATE SCHOOL OF NATURAL AND APPLIED
SCIENCES COMPUTER ENGINEERING
DEPARTMENT 
20192020 SPRING SEMESTER 
Course 
618 DEEP LEARNING (3+0) 

Instructor 
Prof. Dr.
Aybars UĞUR 

Course Place and Time 
EGE University Computer Engineering Department B3 Room 

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 (50%)
Final Exam (50%) 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. 