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  
Oral Exams  
Midterm 
1

30

Final 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 

Theoretical 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  
Oral Exam  
Midterms 
1

15


Final Exam 
1

22


Total 
150

#

Program Competencies/Outcomes 
* Contribution Level


1 
2 
3 
4 
5 

1  To have adequate knowledge in Mathematics, Science, Computer Science and Software Engineering; to be able to use theoretical and applied information in these areas on complex engineering problems. 
X  
2  To be able to identify, define, formulate, and solve complex Software Engineering problems; to be able to select and apply proper analysis and modeling methods for this purpose. 
X  
3  To be able to design, implement, verify, validate, document, measure and maintain a complex software system, process, or product under realistic constraints and conditions, in such a way as to meet the requirements; ability to apply modern methods for this purpose. 
X  
4  To be able to devise, select, and use modern techniques and tools needed for analysis and solution of complex problems in software engineering applications; to be able to use information technologies effectively. 
X  
5  To be able to design and conduct experiments, gather data, analyze and interpret results for investigating complex Software Engineering problems. 
X  
6  To be able to work effectively in Software Engineering disciplinary and multidisciplinary teams; to be able to work individually. 

7  To be able to communicate effectively in Turkish, both orally and in writing; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to be able to present effectively, to be able to give and receive clear and comprehensible instructions. 

8  To have knowledge about global and social impact of engineering practices and software applications on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of Engineering and Software Engineering solutions. 

9  To be aware of ethical behavior, professional and ethical responsibility; to have knowledge about standards utilized in engineering applications. 

10  To have knowledge about industrial practices such as project management, risk management, and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development. 

11  To be able to collect data in the area of Software Engineering, and to be able to communicate with colleagues in a foreign language. 

12  To be able to speak a second foreign language at a medium level of fluency efficiently. 

13  To recognize the need for lifelong learning; to be able to access information, to be able to stay current with developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Software Engineering. 
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest