FACULTY OF ENGINEERING

Department of Mechatronics 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/Spring
3
0
3
5

Prerequisites
None
Course Language
English
Course Type
Elective
Course Level
First Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course Problem Solving
Lecture / Presentation
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.
Learning Outcomes The students who succeeded in this course;
  • Describe deep neural networks and models.
  • Use general architectures and algorithms from deep neural networks.
  • Compare different deep learning algorithms.
  • Apply various deep neural network algorithms to specific problems.
  • Develop deep neural network models and algorithms using computer toolboxes.
Course Description The following topics will be included: feed-forward neural networks, back-propagation, convolutional neural networks, recurrent neural networks, recursive neural networks, regularization, optimization.

 



Course Category

Core Courses
Major Area Courses
Supportive Courses
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

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 2-3. Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613.
3 Applied Math and Machine Learning Basics Chapter 4-5. 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 11-12. Deep Learning. I. Goodfellow, Y. Bengio, A. Courville. ISBN: 9780262035613.
15 Semester Review
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 Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
4
30
Portfolio
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 End-of-Semester Activities on the Final Grade
1
40
Total

ECTS / WORKLOAD TABLE

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
0
Study Hours Out of Class
14
3
42
Field Work
0
Quizzes / Studio Critiques
4
5
20
Portfolio
0
Homework / Assignments
0
Presentation / Jury
0
Project
0
Seminar / Workshop
0
Oral Exam
0
Midterms
1
15
15
Final Exam
1
25
25
    Total
150

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To have knowledge in Mathematics, science, physics knowledge based on mathematics; mathematics with multiple variables, differential equations, statistics, optimization and linear algebra; to be able to use theoretical and applied knowledge in complex engineering problems

2

To be able to identify, define, formulate, and solve complex mechatronics engineering problems; to be able to select and apply appropriate analysis and modeling methods for this purpose.

3

To be able to design a complex electromechanical system, process, device or product with sensor, actuator, control, hardware, and software to meet specific requirements under realistic constraints and conditions; to be able to apply modern design methods for this purpose.

4

To be able to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in Mechatronics Engineering applications; to be able to use information technologies effectively.

5

To be able to design, conduct experiments, collect data, analyze and interpret results for investigating Mechatronics Engineering problems.

6

To be able to work effectively in Mechatronics Engineering disciplinary and multidisciplinary teams; to be able to work individually.

7

To be able to communicate effectively in Turkish, both in oral and written forms; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, 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 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 solutions.

9

To be aware of ethical behavior, professional and ethical responsibility; information on standards used 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

Using a foreign language, he collects information about Mechatronics Engineering and communicates with his colleagues. ("European Language Portfolio Global Scale", Level B1)

12

To be able to use the second foreign language at intermediate level.

13

To recognize the need for lifelong learning; to be able to access information; to be able to follow developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Mechatronics Engineering.

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

 


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