EGE UNIVERSITY GRADUATE SCHOOL OF NATURAL AND APPLIED
SCIENCES COMPUTER ENGINEERING
DEPARTMENT |
2020-2021 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 |
Online (Teams and EGEDERS) (Tuesday, 13:15-16:00) 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. Only for PhD Students. |
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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 Take-Home
and reports, students will be allowed a total of 5 (five) late days; each
additional late day will incur a 10% penalty. |