Department of Computer Engineering

CE 455 | Course Introduction and Application Information

Course Name

Deep Neural Networks

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
CE 455	FALL	3	0	3	5

Prerequisites	None
Course Language	English
Course Type	ELECTIVE_COURSE
Course Level	First Cycle
Mode of Delivery	Face-To-Face
Teaching Methods and Techniques of the Course	Problem Solving Lecture / Presentation
National Occupational Classification Code	-
Course Coordinator	Dr. Öğr. Üyesi Kaya Oğuz
Course Lecturer(s)	-
Assistant(s)	-

Course Objectives

This course provides advanced knowledge about the structures and algorithms of advanced deep neural networks. The theoretical properties of deep neural networks structures and algorithms, as well as practical applications resulting from this theory will be discussed.

Learning Outcomes

The students who succeeded in this course;

Name	Description	PC Sub	* Contribution Level
Name	Description	PC Sub	1	2	3	4	5
LO1	describe deep neural network structures and models.					X
LO2	use general structures and algorithms in the field of deep neural networks.					X
LO3	compare different algorithms in the field of deep learning.					X
LO4	apply various deep neural network algorithms to specific problems.					X
LO5	develop deep neural network models and algorithms using computer toolboxes.					X

Course Description

The course content includes feedforward neural networks, backpropagation, convolutional neural networks, recurrent neural networks, reversible neural networks, regularization, and optimization.

Related Sustainable Development Goals

Course Category	Core Courses
	Major Area Courses	X
	Supportive Courses
	Media and Managment Skills Courses
	Transferable Skill Courses

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week	Subjects	Required Materials	Learning Outcome
1	Introduction	Bölüm 1. Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613.	LO1
2	Applied Math and Machine Learning Basics	Bölüm 2-3. Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613.	LO3
3	Applied Math and Machine Learning Basics	Bölüm 4-5. Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613.	LO1
4	Deep Feedforward Networks	Bölüm 6. Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613.	LO2
5	Regularization for Deep Learning	Bölüm 7. Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613.	LO2
6	Regularization for Deep Learning	Bölüm 7. Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613.	LO2
7	Optimization for Deep Models	Bölüm 8. Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613.	LO2
8	Midterm Exam		-
9	Optimization for Deep Models	Bölüm 8. Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613.	LO2
10	Convolutional Neural Networks	Bölüm 9. Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613.	LO4
11	Convolutional Neural Networks	Bölüm 9. Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613.	LO4
12	Recurrent and Recursive Nets	Bölüm 10 Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613.	LO4
13	Recurrent and Recursive Nets	Bölüm 10 Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613.	LO4
14	Practical Methodology and Applications	Bölüm 11-12. Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613.	LO5
15	Review of the Semester		-
16	Final Exam		-

Course Notes/Textbooks	I. Goodfellow Y. Bengio A. Courville Deep Learning MIT Press 2016 ISBN: 9780262035613.
Suggested Readings/Materials	-

EVALUATION SYSTEM

Semester Activities	Number	Weighting	LO1	LO2	LO3	LO4	LO5
Quizzes / Studio Critiques	4	20	X	X	X	X
Project	1	10				X	X
Midterm	1	30	X	X	X
Final Exam	1	40	X	X	X	X	X
Total	7	100

ECTS / WORKLOAD TABLE

Semester Activities	Number	Duration (Hours)	Workload
Participation	-	-	-
Theoretical Course Hours	16	3	48
Laboratory / Application Hours	-	-	-
Study Hours Out of Class	14	3	42
Field Work	-	-	-
Quizzes / Studio Critiques	4	3	12
Portfolio	-	-	-
Homework / Assignments	-	-	-
Presentation / Jury	-	-	-
Project	1	8	8
Seminar / Workshop	-	-	-
Oral Exams	-	-	-
Midterms	1	15	15
Final Exam	1	25	25
		Total	150

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
			1	2	3	4	5
No program competency data found.

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

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