DEPARTMENT OF SOFTWARE ENGINEERING (ENGLISH)
Qualification Awarded	Program Süresi	Toplam Kredi (AKTS)	Öğretim Şekli	Yeterliliğin Düzeyi ve Öğrenme Alanı
Bachelor's (First Cycle) Degree	4	240	FULL TIME	TYÇ, TR-NQF-HE, EQF-LLL, ISCED (2011):Level 6 QF-EHEA:First Cycle TR-NQF-HE, ISCED (1997-2013): 48,52

Ders Genel Tanıtım Bilgileri

Course Code:

1413002007

Ders İsmi:

Deep Learning

Ders Yarıyılı:

Spring

Ders Kredileri:

Theoretical	Practical	Labs	Credit	ECTS
3	0	0	3	5

Language of instruction:

Ders Koşulu:

Ders İş Deneyimini Gerektiriyor mu?:

Other Recommended Topics for the Course:

none

Type of course:

Department Elective

Course Level:

Bachelor

TR-NQF-HE:6. Master`s Degree

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Dr.Öğr.Üyesi Adem ÖZYAVAŞ

Course Lecturer(s):

Course Assistants:

Dersin Amaç ve İçeriği

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 artificial 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.

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) Students can evaluate common deep learning methods in terms of effectiveness.
2) Students can evaluate the advantages and disadvantages of the deep learning method that is considered to be used.
3) Students can design and test basic deep learning solutions.
4) Students identify and apply the appropriate deep learning architecture and algorithm for the predicted solution.
5) Students have knowledge about editing and optimization methods of deep models.
2 - Skills
Cognitive - Practical
3 - Competences
Communication and Social Competence
Learning Competence
Field Specific Competence
Competence to Work Independently and Take Responsibility

Ders Akış Planı

Week	Subject	Related Preparation
1)	Introduction to pattern recognition	Yoshua Bengio, Ian J. Goodfellow and Aaron Courville, “ Deep Learning”, Book in preparation for MIT Press, http://www.iro.umontreal.ca/~bengioy/dlbook, 2015. Yoshua Bengio, “Learning Deep Architectures for AI”, Foundations and Trends in Machine Learning: Vol. 2: No. 1, pp 1-127, 2009. Li Deng and Dong Yu, "Deep Learning: Methods and Applications", Foundations and Trends in Signal Processing: Vol. 7: No. 3–4, pp 197-387, 2014.
2)	Statistical classifiers	Yoshua Bengio, Ian J. Goodfellow and Aaron Courville, “ Deep Learning”, Book in preparation for MIT Press, http://www.iro.umontreal.ca/~bengioy/dlbook, 2015. Yoshua Bengio, “Learning Deep Architectures for AI”, Foundations and Trends in Machine Learning: Vol. 2: No. 1, pp 1-127, 2009. Li Deng and Dong Yu, "Deep Learning: Methods and Applications", Foundations and Trends in Signal Processing: Vol. 7: No. 3–4, pp 197-387, 2014.
3)	Fukunaga–Koontz transform	Yoshua Bengio, Ian J. Goodfellow and Aaron Courville, “ Deep Learning”, Book in preparation for MIT Press, http://www.iro.umontreal.ca/~bengioy/dlbook, 2015. Yoshua Bengio, “Learning Deep Architectures for AI”, Foundations and Trends in Machine Learning: Vol. 2: No. 1, pp 1-127, 2009. Li Deng and Dong Yu, "Deep Learning: Methods and Applications", Foundations and Trends in Signal Processing: Vol. 7: No. 3–4, pp 197-387, 2014.
4)	Fuzzy classifier	Yoshua Bengio, Ian J. Goodfellow and Aaron Courville, “ Deep Learning”, Book in preparation for MIT Press, http://www.iro.umontreal.ca/~bengioy/dlbook, 2015. Yoshua Bengio, “Learning Deep Architectures for AI”, Foundations and Trends in Machine Learning: Vol. 2: No. 1, pp 1-127, 2009. Li Deng and Dong Yu, "Deep Learning: Methods and Applications", Foundations and Trends in Signal Processing: Vol. 7: No. 3–4, pp 197-387, 2014.
5)	stochastic methods	Yoshua Bengio, Ian J. Goodfellow and Aaron Courville, “ Deep Learning”, Book in preparation for MIT Press, http://www.iro.umontreal.ca/~bengioy/dlbook, 2015. Yoshua Bengio, “Learning Deep Architectures for AI”, Foundations and Trends in Machine Learning: Vol. 2: No. 1, pp 1-127, 2009. Li Deng and Dong Yu, "Deep Learning: Methods and Applications", Foundations and Trends in Signal Processing: Vol. 7: No. 3–4, pp 197-387, 2014.
6)	Size Reduction	Yoshua Bengio, Ian J. Goodfellow and Aaron Courville, “ Deep Learning”, Book in preparation for MIT Press, http://www.iro.umontreal.ca/~bengioy/dlbook, 2015. Yoshua Bengio, “Learning Deep Architectures for AI”, Foundations and Trends in Machine Learning: Vol. 2: No. 1, pp 1-127, 2009. Li Deng and Dong Yu, "Deep Learning: Methods and Applications", Foundations and Trends in Signal Processing: Vol. 7: No. 3–4, pp 197-387, 2014.
7)	optical filters	Yoshua Bengio, Ian J. Goodfellow and Aaron Courville, “ Deep Learning”, Book in preparation for MIT Press, http://www.iro.umontreal.ca/~bengioy/dlbook, 2015. Yoshua Bengio, “Learning Deep Architectures for AI”, Foundations and Trends in Machine Learning: Vol. 2: No. 1, pp 1-127, 2009. Li Deng and Dong Yu, "Deep Learning: Methods and Applications", Foundations and Trends in Signal Processing: Vol. 7: No. 3–4, pp 197-387, 2014.
8)	midterm 1	Yoshua Bengio, Ian J. Goodfellow and Aaron Courville, “ Deep Learning”, Book in preparation for MIT Press, http://www.iro.umontreal.ca/~bengioy/dlbook, 2015. Yoshua Bengio, “Learning Deep Architectures for AI”, Foundations and Trends in Machine Learning: Vol. 2: No. 1, pp 1-127, 2009. Li Deng and Dong Yu, "Deep Learning: Methods and Applications", Foundations and Trends in Signal Processing: Vol. 7: No. 3–4, pp 197-387, 2014.
9)	Classification with optical aliasing filters	Yoshua Bengio, Ian J. Goodfellow and Aaron Courville, “ Deep Learning”, Book in preparation for MIT Press, http://www.iro.umontreal.ca/~bengioy/dlbook, 2015. Yoshua Bengio, “Learning Deep Architectures for AI”, Foundations and Trends in Machine Learning: Vol. 2: No. 1, pp 1-127, 2009. Li Deng and Dong Yu, "Deep Learning: Methods and Applications", Foundations and Trends in Signal Processing: Vol. 7: No. 3–4, pp 197-387, 2014.
10)	Optical Fourier correlation	Yoshua Bengio, Ian J. Goodfellow and Aaron Courville, “ Deep Learning”, Book in preparation for MIT Press, http://www.iro.umontreal.ca/~bengioy/dlbook, 2015. Yoshua Bengio, “Learning Deep Architectures for AI”, Foundations and Trends in Machine Learning: Vol. 2: No. 1, pp 1-127, 2009. Li Deng and Dong Yu, "Deep Learning: Methods and Applications", Foundations and Trends in Signal Processing: Vol. 7: No. 3–4, pp 197-387, 2014.
11)	Common conversion correlation	Yoshua Bengio, Ian J. Goodfellow and Aaron Courville, “ Deep Learning”, Book in preparation for MIT Press, http://www.iro.umontreal.ca/~bengioy/dlbook, 2015. Yoshua Bengio, “Learning Deep Architectures for AI”, Foundations and Trends in Machine Learning: Vol. 2: No. 1, pp 1-127, 2009. Li Deng and Dong Yu, "Deep Learning: Methods and Applications", Foundations and Trends in Signal Processing: Vol. 7: No. 3–4, pp 197-387, 2014.
12)	Adaptive co-conversion correlation	Yoshua Bengio, Ian J. Goodfellow and Aaron Courville, “ Deep Learning”, Book in preparation for MIT Press, http://www.iro.umontreal.ca/~bengioy/dlbook, 2015. Yoshua Bengio, “Learning Deep Architectures for AI”, Foundations and Trends in Machine Learning: Vol. 2: No. 1, pp 1-127, 2009. Li Deng and Dong Yu, "Deep Learning: Methods and Applications", Foundations and Trends in Signal Processing: Vol. 7: No. 3–4, pp 197-387, 2014.
13)	Pattern tracing in sequential images	Yoshua Bengio, Ian J. Goodfellow and Aaron Courville, “ Deep Learning”, Book in preparation for MIT Press, http://www.iro.umontreal.ca/~bengioy/dlbook, 2015. Yoshua Bengio, “Learning Deep Architectures for AI”, Foundations and Trends in Machine Learning: Vol. 2: No. 1, pp 1-127, 2009. Li Deng and Dong Yu, "Deep Learning: Methods and Applications", Foundations and Trends in Signal Processing: Vol. 7: No. 3–4, pp 197-387, 2014.
14)	Pattern recognition performance measures	Yoshua Bengio, Ian J. Goodfellow and Aaron Courville, “ Deep Learning”, Book in preparation for MIT Press, http://www.iro.umontreal.ca/~bengioy/dlbook, 2015. Yoshua Bengio, “Learning Deep Architectures for AI”, Foundations and Trends in Machine Learning: Vol. 2: No. 1, pp 1-127, 2009. Li Deng and Dong Yu, "Deep Learning: Methods and Applications", Foundations and Trends in Signal Processing: Vol. 7: No. 3–4, pp 197-387, 2014.
15)	Pattern recognition performance measures	Yoshua Bengio, Ian J. Goodfellow and Aaron Courville, “ Deep Learning”, Book in preparation for MIT Press, http://www.iro.umontreal.ca/~bengioy/dlbook, 2015. Yoshua Bengio, “Learning Deep Architectures for AI”, Foundations and Trends in Machine Learning: Vol. 2: No. 1, pp 1-127, 2009. Li Deng and Dong Yu, "Deep Learning: Methods and Applications", Foundations and Trends in Signal Processing: Vol. 7: No. 3–4, pp 197-387, 2014.
16)	Final	Yoshua Bengio, Ian J. Goodfellow and Aaron Courville, “ Deep Learning”, Book in preparation for MIT Press, http://www.iro.umontreal.ca/~bengioy/dlbook, 2015. Yoshua Bengio, “Learning Deep Architectures for AI”, Foundations and Trends in Machine Learning: Vol. 2: No. 1, pp 1-127, 2009. Li Deng and Dong Yu, "Deep Learning: Methods and Applications", Foundations and Trends in Signal Processing: Vol. 7: No. 3–4, pp 197-387, 2014.

Sources

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.
Yoshua Bengio, “Learning Deep Architectures for AI”, Foundations and Trends in Machine Learning: Vol. 2: No. 1, pp 1-127, 2009.
Li Deng and Dong Yu, "Deep Learning: Methods and Applications", Foundations and Trends in Signal Processing: Vol. 7: No. 3–4, pp 197-387, 2014.

References:

Ders - Program Öğrenme Kazanım İlişkisi

Ders Öğrenme Kazanımları	1	2	3	4	5
Program Outcomes
1) Knowledge of mathematics, science, basic engineering, computer computing, and engineering discipline-specific topics; ability to use this knowledge in solving complex engineering problems
2) Sufficient knowledge of issues related to software engineering; theoretical and To be able to use applied knowledge in solving algorithmic and software problems Skill.
3) Ability to define, formulate and analyze complex engineering problems using basic science, mathematics and engineering knowledge and taking into account the UN Sustainable Development Goals relevant to the problem under consideration.
4) Ability to design creative solutions to complex engineering problems; The ability to design complex systems, processes, devices or products to meet current and future requirements, taking into account realistic constraints and conditions.
5) Ability to choose and use appropriate techniques, resources, modern engineering computational tools for the analysis, solution, prediction and modelling of complex engineering problems.
6) Ability to use research methods to examine complex engineering problems, including researching literature, designing experiments, conducting experiments, collecting data, analyzing and interpreting results.
7) Information about the effects of engineering practices on society, health and safety, economy, sustainability and the environment within the scope of the UN Sustainable Development Goals; Awareness of the legal consequences of engineering solutions
8) Acting in accordance with engineering professional principles and knowledge about ethical responsibility; Awareness of acting impartially, without discrimination on any issue, and being inclusive of diversity.
9) Ability to work effectively as a team member or leader in intradisciplinary and multidisciplinary teams (face-to-face, remote or hybrid).
10) Individual working ability.
11) Ability to communicate effectively verbally and in writing on technical issues, taking into account the various differences of the target audience (such as education, language, profession).
12) Knowledge of business practices such as project management and economic feasibility analysis
13) Awareness about entrepreneurship and innovation.
14) A lifelong learning skill that includes being able to learn independently and continuously, adapting to new and developing technologies, and thinking inquisitively about technological changes.

Ders - Öğrenme Kazanımı İlişkisi

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Knowledge of mathematics, science, basic engineering, computer computing, and engineering discipline-specific topics; ability to use this knowledge in solving complex engineering problems
2)	Sufficient knowledge of issues related to software engineering; theoretical and To be able to use applied knowledge in solving algorithmic and software problems Skill.
3)	Ability to define, formulate and analyze complex engineering problems using basic science, mathematics and engineering knowledge and taking into account the UN Sustainable Development Goals relevant to the problem under consideration.
4)	Ability to design creative solutions to complex engineering problems; The ability to design complex systems, processes, devices or products to meet current and future requirements, taking into account realistic constraints and conditions.
5)	Ability to choose and use appropriate techniques, resources, modern engineering computational tools for the analysis, solution, prediction and modelling of complex engineering problems.
6)	Ability to use research methods to examine complex engineering problems, including researching literature, designing experiments, conducting experiments, collecting data, analyzing and interpreting results.
7)	Information about the effects of engineering practices on society, health and safety, economy, sustainability and the environment within the scope of the UN Sustainable Development Goals; Awareness of the legal consequences of engineering solutions
8)	Acting in accordance with engineering professional principles and knowledge about ethical responsibility; Awareness of acting impartially, without discrimination on any issue, and being inclusive of diversity.
9)	Ability to work effectively as a team member or leader in intradisciplinary and multidisciplinary teams (face-to-face, remote or hybrid).
10)	Individual working ability.
11)	Ability to communicate effectively verbally and in writing on technical issues, taking into account the various differences of the target audience (such as education, language, profession).
12)	Knowledge of business practices such as project management and economic feasibility analysis
13)	Awareness about entrepreneurship and innovation.
14)	A lifelong learning skill that includes being able to learn independently and continuously, adapting to new and developing technologies, and thinking inquisitively about technological changes.

Öğrenme Etkinliği ve Öğretme Yöntemleri

Course
Grup çalışması ve ödevi
Homework
Problem Çözme
Uygulama (Modelleme, Tasarım, Maket, Simülasyon, Deney vs.)

Ölçme ve Değerlendirme Yöntemleri ve Kriterleri

Yazılı Sınav (Açık uçlu sorular, çoktan seçmeli, doğru yanlış, eşleştirme, boşluk doldurma, sıralama)
Homework
Uygulama
Bireysel Proje

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Homework Assignments	4	% 5
Project	1	% 30
Midterms	1	% 25
Semester Final Exam	1	% 40
total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
total		% 100

İş Yükü ve AKTS Kredisi Hesaplaması

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
Quizzes	4	9	36
Midterms	1	30	30
Final	1	30	30
Total Workload			255