EGE UNIVERSITY GRADUATE SCHOOL OF NATURAL AND APPLIED
SCIENCES COMPUTER ENGINEERING
DEPARTMENT |
2023-2024 SPRING SEMESTER |
Course |
618 DEEP LEARNING (3+0) |
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Instructor |
Prof. Dr. Aybars UĞUR |
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Course Place and Time |
B3 Class (Monday, 13:30-16:00) EGE University
Computer Engineering Department |
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Learning Outcomes |
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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. |
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Syllabus |
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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) |
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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. |
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Links |
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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.
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Grading |
Given In EgeDers
Course Page For the Take-Home
and reports, students will be allowed a total of 5 (five) late days; each
additional late day will incur a 10% penalty. |