Course Name 
Deep Neural Networks

Code

Semester

Theory
(hour/week) 
Application/Lab
(hour/week) 
Local Credits

ECTS

CE 455

Fall/Spring

3

0

3

5

Prerequisites 
None


Course Language 
English


Course Type 
Elective


Course Level 
First Cycle


Course Coordinator  
Course Lecturer(s)  
Assistant(s)   
Course Objectives  This course provides review of the state of the art in deep learning and neural networks. Both theoretical aspects of deep neural network structures and algorithms as well as practical applications originating from theory will be discussed. 
Course Description 
The students who succeeded in this course;

Course Content  The following topics will be included: feedforward neural networks, backpropagation, convolutional neural networks, recurrent neural networks, recursive neural networks, regularization, optimization. 

Core Courses  
Major Area Courses  
Supportive Courses  
Media and Management Skills Courses  
Transferable Skill Courses 
Week  Subjects  Related Preparation 
1  Introduction  Chapter 1. Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613. 
2  Applied Math and Machine Learning Basics  Chapter 23. Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613. 
3  Applied Math and Machine Learning Basics  Chapter 45. Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613. 
4  Deep Feedforward Networks  Chapter 6. Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613. 
5  Regularization for Deep Learning  Chapter 7. Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613. 
6  Regularization for Deep Learning  Chapter 7. Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613. 
7  Optimization for Deep Models  Chapter 8. Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613. 
8  Optimization for Deep Models  Chapter 8. Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613. 
9  Midterm Exam  
10  Convolutional Networks  Chapter 9. Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613. 
11  Convolutional Networks  Chapter 9. Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613. 
12  Recurrent and Recursive Nets  Chapter 10 Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613. 
13  Recurrent and Recursive Nets  Chapter 10 Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613. 
14  Practical Methodology and Applications  Chapter 1112. Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613. 
15  Deep Generative Models  Chapter 20. Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613. 
16  General review of semester 
Course Notes/Textbooks  I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press, 2016, ISBN: 9780262035613 
Suggested Readings/Materials 
Semester Activities  Number  Weigthing 
Participation  
Laboratory / Application  
Field Work  
Quizzes / Studio Critiques 
4

30

Homework / Assignments  
Presentation / Jury  
Project  
Seminar / Workshop  
Portfolios  
Midterms / Oral Exams 
1

30

Final / Oral Exam 
1

40

Total 
Weighting of Semester Activities on the Final Grade  5 
60 
Weighting of EndofSemester Activities on the Final Grade  1 
40 
Total 
Semester Activities  Number  Duration (Hours)  Workload 

Course Hours Including exam week: 16 x total hours 
16

3

48

Laboratory / Application Hours Including exam week: 16 x total hours 
16


Study Hours Out of Class 
15

3


Field Work  
Quizzes / Studio Critiques 
4

5


Homework / Assignments  
Presentation / Jury  
Project  
Seminar / Workshop  
Portfolios  
Midterms / Oral Exams 
1

15


Final / Oral Exam 
1

22


Total 
150

#

Program Competencies/Outcomes 
* Contribution Level


1 
2 
3 
4 
5 

1  Adequate knowledge in Mathematics, Science and Software Engineering; ability to use theoretical and applied information in these areas to model and solve Software Engineering problems  
2  Ability to identify, define, formulate, and solve complex Software Engineering problems; ability to select and apply proper analysis and modeling methods for this purpose  
3  Ability to design, implement, verify, validate, measure and maintain a complex software system, process or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern methods for this purpose  
4  Ability to devise, select, and use modern techniques and tools needed for Software Engineering practice  
5  Ability to design and conduct experiments, gather data, analyze and interpret results for investigating Software Engineering problems  
6  Ability to work efficiently in Software Engineering disciplinary and multidisciplinary teams; ability to work individually  
7  Ability to communicate effectively in Turkish, both orally and in writing; knowledge of a minimum of two foreign languages  
8  Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself  
9  Awareness of professional and ethical responsibility  
10  Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development  
11  Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of Software Engineering solutions 
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest