COMPUTER ENGINEERING | |||||
Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 |
Course Code: | 1410002003 | ||||||||
Ders İsmi: | Deep Learning | ||||||||
Ders Yarıyılı: | Spring | ||||||||
Ders Kredileri: |
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Language of instruction: | TR | ||||||||
Ders Koşulu: | |||||||||
Ders İş Deneyimini Gerektiriyor mu?: | No | ||||||||
Type of course: | Bölüm Seçmeli | ||||||||
Course Level: |
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Mode of Delivery: | Face to face | ||||||||
Course Coordinator : | Dr.Öğr.Üyesi Recep DURANAY | ||||||||
Course Lecturer(s): |
Prof. Dr. Haluk GÜMÜŞKAYA |
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Course Assistants: |
Course Objectives: | Presenting methods that can be used to learn high-level features obtained from different types of data by utilizing deep architectures and showing how these methods can be applied for different purposes from image recognition to robot control. |
Course Content: | History and theoretical advantages of deep learning, Basic neural network architectures and learning algorithms that can be used for deep learning, Arrangement of Distributed Models, Optimization Techniques for Training Deep Models, Convolutional networks, Feedback and recursive networks, Autoencoders and Linear Factor Models, Learning by Representation, Deep Productive Models – Boltzman Machines. |
The students who have succeeded in this course;
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Week | Subject | Related Preparation |
1) | Introduction – History and Theoretical Foundations | |
2) | Mathematical Fundamentals: Linear Algebra, Probability and Information Theory lesson | |
3) | Artificial Neural Networks Fundamentals | |
4) | Feed Forward Deep Networks | |
5) | Editing Deep or Distributed Models | |
6) | Optimization Techniques for Training Deep Models | |
7) | Convolutional Networks | |
8) | Midterm | |
9) | Autoencoders and Linear Factor Models | |
10) | Autoencoders and Linear Factor Models | Course Book |
11) | Learning through Representation | |
12) | Learning through Representation | |
13) | Deep Productive Models | |
14) | Deep Productive Models | Course Book |
15) | Boltzman Machines | |
16) | Final |
Course Notes / Textbooks: | Yoshua Bengio, Ian J. Goodfellow and Aaron Courville, “ Deep Learning”, Book in preparation for MIT Press,http://www.iro.umontreal.ca/~bengioy/dlbook, 2015. |
References: | Yoshua Bengio, Ian J. Goodfellow and Aaron Courville, “ Deep Learning”, Book in preparation for MIT Press,http://www.iro.umontreal.ca/~bengioy/dlbook, 2015. |
Ders Öğrenme Kazanımları | 1 |
2 |
3 |
4 |
5 |
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Program Outcomes | ||||||||||||||||
1) PO 1.1) Sufficient knowledge in mathematics, science and computer engineering | ||||||||||||||||
2) PO 1.2) Ability to apply theoretical and applied knowledge in mathematics, science and computer engineering for modeling and solving engineering problems. | ||||||||||||||||
3) PO 2.1) Identifying complex engineering problems | ||||||||||||||||
4) PO 2.2) Defining complex engineering problems | ||||||||||||||||
5) PO 2.3) Formulating complex engineering problems | ||||||||||||||||
6) PO 2.4) Ability to solve complex engineering problems | ||||||||||||||||
7) PO 2.5) Ability to choose and apply appropriate analysis and modeling methods | ||||||||||||||||
8) PO 3.1) Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions. | ||||||||||||||||
9) PO 3.2) Ability to apply modern design methods under realistic constraints and conditions for a complex system, process, device or product | ||||||||||||||||
10) PO 4.1) Developing modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications | ||||||||||||||||
11) PO 4.2) Ability to select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications | ||||||||||||||||
12) PO 4.3) Ability to use information technologies effectively. | ||||||||||||||||
13) PO 5.1) Examination of complex engineering problems or discipline-specific research topics, designing experiments | ||||||||||||||||
14) PO 5.2) Examination of complex engineering problems or discipline-specific research topics, experimentation | ||||||||||||||||
15) PO 5.3 ) Analysis of complex engineering problems or discipline-specific research topics, data collection | ||||||||||||||||
16) PO 5.4) Analyzing the results of complex engineering problems or discipline-specific research topics | ||||||||||||||||
17) PO 5.5) Examining and interpreting complex engineering problems or discipline-specific research topics |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | PO 1.1) Sufficient knowledge in mathematics, science and computer engineering | |
2) | PO 1.2) Ability to apply theoretical and applied knowledge in mathematics, science and computer engineering for modeling and solving engineering problems. | |
3) | PO 2.1) Identifying complex engineering problems | |
4) | PO 2.2) Defining complex engineering problems | |
5) | PO 2.3) Formulating complex engineering problems | |
6) | PO 2.4) Ability to solve complex engineering problems | |
7) | PO 2.5) Ability to choose and apply appropriate analysis and modeling methods | |
8) | PO 3.1) Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions. | |
9) | PO 3.2) Ability to apply modern design methods under realistic constraints and conditions for a complex system, process, device or product | |
10) | PO 4.1) Developing modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications | |
11) | PO 4.2) Ability to select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications | |
12) | PO 4.3) Ability to use information technologies effectively. | 5 |
13) | PO 5.1) Examination of complex engineering problems or discipline-specific research topics, designing experiments | |
14) | PO 5.2) Examination of complex engineering problems or discipline-specific research topics, experimentation | |
15) | PO 5.3 ) Analysis of complex engineering problems or discipline-specific research topics, data collection | |
16) | PO 5.4) Analyzing the results of complex engineering problems or discipline-specific research topics | |
17) | PO 5.5) Examining and interpreting complex engineering problems or discipline-specific research topics |
Course | |
Homework |
Yazılı Sınav (Açık uçlu sorular, çoktan seçmeli, doğru yanlış, eşleştirme, boşluk doldurma, sıralama) | |
Bireysel Proje | |
Sunum |
Semester Requirements | Number of Activities | Level of Contribution |
total | % | |
PERCENTAGE OF SEMESTER WORK | % 0 | |
PERCENTAGE OF FINAL WORK | % | |
total | % |
Activities | Number of Activities | Duration (Hours) | Workload |
Course Hours | 13 | 3 | 39 |
Study Hours Out of Class | 13 | 2 | 26 |
Presentations / Seminar | 1 | 18 | 18 |
Project | 1 | 40 | 40 |
Homework Assignments | 4 | 9 | 36 |
Midterms | 1 | 30 | 30 |
Final | 1 | 30 | 30 |
Total Workload | 219 |