FACULTY OF ENGINEERING
Department of Industrial Engineering
SOCIAL MEDIA
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 SolvingLecture / 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;
|
| 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. |
|
|
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 adequate knowledge in Mathematics, Science and Industrial Engineering; to be able to use theoretical and applied information in these areas to model and solve Industrial Engineering problems. |
|||||
| 2 | To be able to identify, formulate and solve complex Industrial Engineering problems by using state-of-the-art methods, techniques and equipment; to be able to select and apply proper analysis and modeling methods for this purpose. |
|||||
| 3 | To be able to analyze a complex system, process, device or product, and to design with realistic limitations to meet the requirements using modern design techniques. |
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| 4 | To be able to choose and use the required modern techniques and tools for Industrial Engineering applications; to be able to use information technologies efficiently. |
|||||
| 5 | To be able to design and do simulation and/or experiment, collect and analyze data and interpret the results for investigating Industrial Engineering problems and Industrial Engineering related research areas. |
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| 6 | To be able to work efficiently in Industrial 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 present effectively; to be able to give and receive clear and comprehensible instructions |
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| 8 | To have knowledge about contemporary issues and the global and societal effects of Industrial Engineering practices on health, environment, and safety; to be aware of the legal consequences of Industrial Engineering solutions. |
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| 9 | To be aware of professional and ethical responsibility; to have knowledge of the standards used in Industrial Engineering practice. |
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| 10 | To have knowledge about business life practices such as project management, risk management, and change management; to be aware of entrepreneurship and innovation; to have knowledge about sustainable development. |
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| 11 | To be able to collect data in the area of Industrial Engineering; to be able to communicate with colleagues in a foreign language. |
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| 12 | To be able to speak a second foreign at a medium level of fluency efficiently. |
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| 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 Industrial Engineering. |
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*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
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