COMPUTER 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:

1415002038

Ders İsmi:

Generative Artificial Intelligence

Ders Yarıyılı:

Fall

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 :

Assoc. Prof. Oğuz ATA

Course Lecturer(s):

Course Assistants:

Dersin Amaç ve İçeriği

Course Objectives:

Understand the fundamental principles and architectures of generative models, including VAEs, GANs, Transformers, and Duffision models.

Develop Implementation Skills: Gain hands-on experience in coding and deploying various generative models using frameworks like TensorFlow or PyTorch.

Explore Creative Applications: Learn how to apply generative AI techniques in creative domains such as digital art, music generation, and interactive media.

Examine Ethical Considerations: Discuss the ethical implications of generative AI, including issues of bias, fairness, and the potential impacts on privacy and authenticity.

Encourage Innovation and Research: Foster skills in experimental design and research methodologies to enable students to innovate and contribute original ideas to the field of generative AI.

Course Content:

Introduction to AI, ML, DL and Generative AI, Working with Pre-trained AI Models, Fundamentals of Text Generation, Image Generation with AI, AI-Generated Music and Audio, AI for Video Generation and Animation, Multimodal AI - Text, Image, and Audio Together, Fine-Tuning AI for Personalized Content, Selected Use Cases: Image Captioning with Generative AI, Create Your Own ChatGPT-Like Website, Create a Voice Assistant, Generative AI-Powered Meeting AssistantSummarize Your Private Data with , AI and RAG, Universal Language Translator with LMM and STT TSS.

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) Ethical and Societal Implications: Graduates will develop a critical awareness of the ethical, legal, and societal implications of using and deploying generative AI technologies. This includes grappling with issues related to the authenticity and ethical use of synthetic media.
2 - Skills
Cognitive - Practical
1) Proficiency in Generative Models: Students will gain a thorough understanding of various generative models. They will be able to learn and apply these models in different domains.
2) Practical Implementation Skills: By the end of the course, students will be adept at implementing, training, and refining generative models using frameworks and platforms such as TensorFlow, PyTorch and Hugging Face. They will have hands-on experience through projects and assignments that involve generating images, text, or other types of media.
3) Creative and Innovative Applications: Students will learn how to apply generative AI techniques creatively across various domains, such as art, design, media, and entertainment. This includes understanding how to integrate AI into creative processes to enhance or produce novel works.
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)	Course Overview and Introduction, Introduction to AI, ML, DL and Generative AI, Working with Pre-trained AI Models
2)	Fundamentals of Text Generation
3)	Image Generation with AI
4)	AI-Generated Music and Audio
5)	AI for Video Generation and Animation
6)	Multimodal AI: Text, Image, and Audio Together
7)	Fine-Tuning AI for Personalized Content
8)	Selected Use Cases: Image Captioning with Generative AI
9)	Selected Use Cases:: Create Your Own ChatGPT-Like Website
10)	Selected Use Cases: Create a Voice Assistant
11)	Selected Use Cases:: Generative AI-Powered Meeting Assistant
12)	Selected Use Cases: Summarize Your Private Data with , AI and RAG
13)	Selected Use Cases: Universal Language Translator with LMM and STT TSS
14)	Ethical AI and Responsible AI Usage

Sources

Course Notes / Textbooks:	Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play, D. Foster, O'Reilly, 2023. Hands-On Generative AI with Python and TensorFlow 2: Create Next-Generation AI-Generated Content Using State-of-the-Art Techniques, J. Babcock, R. Bali, Packt Publishing, 2022.
References:	Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play, D. Foster, O'Reilly, 2023. Hands-On Generative AI with Python and TensorFlow 2: Create Next-Generation AI-Generated Content Using State-of-the-Art Techniques, J. Babcock, R. Bali, Packt Publishing, 2022.

Ders - Program Öğrenme Kazanım İlişkisi

Ders Öğrenme Kazanımları	1	2	3	4
Program Outcomes
1) 1.1 Sufficient knowledge of subjects such as mathematics and science
2) 1.2 Ability to apply theoretical and applied knowledge in mathematics, science and computer engineering for modeling and solving engineering problems.
3) 1.3 Ability to use theoretical and applied knowledge in fields such as mathematics and science in complex engineering problems.
4) 2.1 Ability to identify, define, formulate and solve complex engineering problems
5) 2.2 Ability to select and apply appropriate analysis and modeling methods for this purpose
6) 3.1 Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions
7) 3.2 Ability to apply modern design methods for this purpose
8) 4.1 Ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications.
9) 4.2 Ability to use information technologies effectively
10) 5.1 Ability to design experiments to examine complex engineering problems or discipline-specific research issues
11) 5.2 Ability to conduct experiments to examine complex engineering problems or discipline-specific research topics
12) 5.3 Ability to collect data to examine complex engineering problems or discipline-specific research topics
13) 5.4 Ability to analyze and interpret experimental results for the study of complex engineering problems or discipline-specific research issues
14) 6.1 Ability to work individually within the discipline
15) 6.2 Ability to work effectively in interdisciplinary teams
16) 6.3 Ability to work effectively in multidisciplinary teams
17) 7.1 Ability to communicate effectively and make presentations both verbally and in Turkish
18) 7.2 Knowledge of at least one foreign language
19) 7.3 Ability to write effective reports and understand written reports
20) 7.4 Ability to prepare design and production reports
21) 7.5 Ability to give and receive clear and understandable instructions
22) 8.1 Awareness of the necessity of lifelong learning
23) 8.2 The ability to access information, follow developments in science and technology and constantly renew oneself
24) 9.1 Acting in accordance with ethical principles, awareness of professional and ethical responsibility
25) 9.2 Information about standards used in engineering applications
26) 10.2 Girişimcilik, yenilikçilik hakkında farkındalık
27) 10.2 Awareness about entrepreneurship, innovation
28) 10.3 Information about sustainable development
29) 11.1 Information about the effects of engineering practices on health, environment and security at universal and social dimensions and the problems of the age reflected in the field of engineering
30) 11.2 Awareness of the legal consequences of engineering solutions
31) 12.1 Having knowledge about discrete mathematics

Ders - Öğrenme Kazanımı İlişkisi

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

	Program Outcomes	Level of Contribution
1)	1.1 Sufficient knowledge of subjects such as mathematics and science
2)	1.2 Ability to apply theoretical and applied knowledge in mathematics, science and computer engineering for modeling and solving engineering problems.
3)	1.3 Ability to use theoretical and applied knowledge in fields such as mathematics and science in complex engineering problems.
4)	2.1 Ability to identify, define, formulate and solve complex engineering problems
5)	2.2 Ability to select and apply appropriate analysis and modeling methods for this purpose
6)	3.1 Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions
7)	3.2 Ability to apply modern design methods for this purpose
8)	4.1 Ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications.
9)	4.2 Ability to use information technologies effectively
10)	5.1 Ability to design experiments to examine complex engineering problems or discipline-specific research issues
11)	5.2 Ability to conduct experiments to examine complex engineering problems or discipline-specific research topics
12)	5.3 Ability to collect data to examine complex engineering problems or discipline-specific research topics
13)	5.4 Ability to analyze and interpret experimental results for the study of complex engineering problems or discipline-specific research issues
14)	6.1 Ability to work individually within the discipline
15)	6.2 Ability to work effectively in interdisciplinary teams
16)	6.3 Ability to work effectively in multidisciplinary teams
17)	7.1 Ability to communicate effectively and make presentations both verbally and in Turkish
18)	7.2 Knowledge of at least one foreign language
19)	7.3 Ability to write effective reports and understand written reports
20)	7.4 Ability to prepare design and production reports
21)	7.5 Ability to give and receive clear and understandable instructions
22)	8.1 Awareness of the necessity of lifelong learning
23)	8.2 The ability to access information, follow developments in science and technology and constantly renew oneself
24)	9.1 Acting in accordance with ethical principles, awareness of professional and ethical responsibility
25)	9.2 Information about standards used in engineering applications
26)	10.2 Girişimcilik, yenilikçilik hakkında farkındalık
27)	10.2 Awareness about entrepreneurship, innovation
28)	10.3 Information about sustainable development
29)	11.1 Information about the effects of engineering practices on health, environment and security at universal and social dimensions and the problems of the age reflected in the field of engineering
30)	11.2 Awareness of the legal consequences of engineering solutions
31)	12.1 Having knowledge about discrete mathematics

Öğrenme Etkinliği ve Öğretme Yöntemleri

Course
Labs
Okuma
Homework
Problem Çözme
Soru cevap/ Tartışma
Web Tabanlı Öğrenme

Ölçme ve Değerlendirme Yöntemleri ve Kriterleri

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

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Homework Assignments	6	% 30
Project	1	% 30
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	14	3	42
Study Hours Out of Class	14	7	98
Midterms	1	2	2
Final	1	3	3
Total Workload			145