Course Details

Course Information

Semester	Course Unit Code	Course Unit Title	T+P+L	Credit	Number of ECTS Credits	Last Updated Date
1	ECE661	DEEP LEARNING	3+0+0	3	7,5	14.05.2025

Course Details

Language of Instruction	English
Level of Course Unit	Doctorate's Degree
Department / Program	ELECTRICAL AND COMPUTER ENGINEERING
Type of Program	Formal Education
Type of Course Unit	Elective
Course Delivery Method	Face To Face
Objectives of the Course	O1. Gain an understanding of deep learning architectures O2. Learn the techniques used for developing deep learning models O3. Develop skills for practical aspects of deep learning O4. Apply the concepts learned to a real problem
Course Content	This course provides an introduction to deep learning. It covers deep architectures for multi-layer perceptrons, convolutional neural networks, and recurrent neural networks. Methods will be implemented by a software and applied on various machine learning problems.
Course Methods and Techniques	Regularization for deep learning Optimization for training deep models Convolutional networks Recurrent and recursive networks Practical methodology of deep learning Deep learning applications
Prerequisites and co-requisities	None
Course Coordinator	None
Name of Lecturers	Associate Prof.Dr. Zafer Aydın Avesis zafer.aydin@agu.edu.tr
Assistants	None
Work Placement(s)	No

Recommended or Required Reading

Resources	1. Deep Learning, I. Goodfellow, Y. Bengio, A. Courville, F. Bach, MIT Press, 2016. 2. Hands-On Machine Learning with Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems, A. Geron, O’Reilly Media, 2017.
Course Notes	1. Deep Learning, I. Goodfellow, Y. Bengio, A. Courville, F. Bach, MIT Press, 2016. 2. Hands-On Machine Learning with Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems, A. Geron, O’Reilly Media, 2017.
Documents	Wil be shared on canvas
Assignments	Wil be shared on canvas
Exams	Wil be shared on canvas

Course Category

Mathematics and Basic Sciences	%20
Engineering	%20
Engineering Design	%20
Social Sciences	%0
Education	%0
Science	%20
Health	%0
Field	%20

Planned Learning Activities and Teaching Methods

Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria

In-Term Studies	Quantity	Percentage
Yarıyıl İçi Çalışmalarının Başarı Notunun Katkısı	1	% 20
Quiz/Küçük Sınav	1	% 10
Ödev	1	% 25
Proje/Çizim	1	% 25
Final examination	1	% 20
Total	5	% 100

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration	Total Work Load
Proje	1	88	88
Yüz Yüze Ders	14	3	42
Ders Dışı Ara Sınav	1	20	20
Final Sınavı	1	30	30
Total Work Load			Number of ECTS Credits 7,5 180

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:

No	Learning Outcomes
1	Explain the mathematical and algorithmic principles of deep learning models
2	Solve a machine learning problem using deep learning methods
3	Implement a deep learning model using a software
4	Apply a deep learning method to a real problem

Weekly Detailed Course Contents

Week	Topics	Study Materials	Materials
1	Deep Feedforward Networks Activity: readings
2	Deep Feedforward Networks Activity: readings, quiz 1
3	Regularization for Deep Learning Activity: readings, homework 1
4	Optimization for Training Deep Models Activity: readings, quiz 2
5	Convolutional Networks Activity: readings, homework 2
6	Convolutional Networks Activity: readings, quiz 3
7	Midterm Exam
8	Semester break
9	Recurrent and Recursive Networks Activity: readings, homework 3
10	Recurrent and Recursive Networks Activity: readings, quiz 4
11	Deep autoencoders Activity: readings, homework 4
12	Deep generative models Activity: readings, quiz 5, homework 5
13	Deep generative models Activity: readings, homework 6
14	Deep generative models Activity: readings, homework 7

Contribution of Learning Outcomes to Programme Outcomes

Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=77911&lang=en