BİLGİ PAKETİ / DERS KATALOĞU
| INFORMATION TECHNOLOGIES (MASTER) (WITH THESIS) (ENGLISH) | |||||
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| Qualification Awarded | Program Süresi | Toplam Kredi (AKTS) | Öğretim Şekli | Yeterliliğin Düzeyi ve Öğrenme Alanı | |
| Master's ( Second Cycle) Degree | 2 | 120 | FULL TIME |
TYÇ, TR-NQF-HE, EQF-LLL, ISCED (2011):Level 7 QF-EHEA:Second Cycle TR-NQF-HE, ISCED (1997-2013): 44,46,48,52,72 |
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Ders Genel Tanıtım Bilgileri
| Course Code: | 3000004002 | ||||||||||
| Ders İsmi: | Cloud Computing | ||||||||||
| Ders Yarıyılı: | Spring | ||||||||||
| Ders Kredileri: |
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| Language of instruction: | EN | ||||||||||
| Ders Koşulu: | |||||||||||
| Ders İş Deneyimini Gerektiriyor mu?: | No | ||||||||||
| Other Recommended Topics for the Course: | |||||||||||
| Type of course: | Department Elective | ||||||||||
| Course Level: |
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| Mode of Delivery: | Face to face | ||||||||||
| Course Coordinator : | Prof. Dr. Haluk GÜMÜŞKAYA | ||||||||||
| Course Lecturer(s): | |||||||||||
| Course Assistants: |
Dersin Amaç ve İçeriği
| Course Objectives: | This is a graduate-level introductory course in Cloud Computing. It makes a broad introduction to many concepts and algorithms, theory and practical work in Cloud Computing. The basic concepts and the selected Cloud Computing topics, algorithms and applications are presented. |
| Course Content: | Distributed System Models and Enabling Technologies, Computer Clusters for Scalable Computing, Virtual Machines and Virtualization of Clusters and Datacenters, Cloud Platform Architecture over Virtualized Data Centers: Data Center Design and Networking, Cloud Computing Service Models, Major Cloud Service Providers. Service Oriented Architectures. Cloud Programming and Software Environments: MapReduce and Hadoop Framework, Grid Computing, Internet of Things. |
Learning Outcomes
The students who have succeeded in this course;
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Ders Akış Planı
| Week | Subject | Related Preparation |
| 1) | Distributed System Models and Enabling Technologies | |
| 2) | Computer Clusters for Scalable Computing | |
| 3) | Virtual Machines and Virtualization of Clusters and Datacenters | |
| 4) | Cloud Platform Architecture over Virtualized Data Centers | |
| 5) | Data Center Design and Networking | |
| 6) | Cloud Computing Service Models | |
| 7) | Major Cloud Service Providers | |
| 8) | Midterm Exam | |
| 9) | Service Oriented Architectures for Distributed Computing | |
| 10) | Cloud Programming and Software Environments | |
| 11) | MapReduce and Hadoop Framework | |
| 12) | Hadoop Framework | |
| 13) | Internet of Things | |
| 14) | Project Demonstration / Midterm Exam II |
Sources
| Course Notes / Textbooks: | Distributed and Cloud Computing: From Parallel Processing to The Internet of Things, K. Hwang, G. Fox and J. Dongarra, Morgan Kaufmann Publishers, 2012. |
| References: | Distributed and Cloud Computing: From Parallel Processing to The Internet of Things, K. Hwang, G. Fox and J. Dongarra, Morgan Kaufmann Publishers, 2012. |
Ders - Program Öğrenme Kazanım İlişkisi
| Ders Öğrenme Kazanımları | 1 |
2 |
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| Program Outcomes | |||||||||||
| 1) Ability to use and apply current technical concepts and practices in the information technologies of software engineering, data management and computer security. | |||||||||||
| 2) Understanding user needs, analyzing them, and using them in the selection, evaluation, and management of computer-based systems. | |||||||||||
| 3) Ability to use data structures and develop algorithms. | |||||||||||
| 4) Ability to analyze and interpret complex big data systems. | |||||||||||
| 5) Ability to interpret and apply concepts and algorithms in machine learning. | |||||||||||
| 6) Understanding of the mathematical foundations of deep learning in the field of data analysis and the ability to apply the theory. | |||||||||||
| 7) Ability to solve complex data structures, develop and apply deep learning models, and interpret artificial intelligence-focused research on these topics. | |||||||||||
| 8) Ability to apply deep learning techniques and interpret real-world datasets and projects to solve problems in image analysis, natural language processing, and recommendation systems. | |||||||||||
| 9) Ability to transfer the basic principles and mathematical infrastructure of digital signal processing to practical applications. | |||||||||||
| 10) Gaining knowledge about the tools and technologies used via the Internet and the different technologies used for server coding languages and tools. | |||||||||||
| 11) Ability to understand of how genes function in multicellular species, the flow of genetic information in single-cell organisms, and the ability to interpret and apply biotechnology applications. | |||||||||||
| 12) Being aware of ethical values and understanding the need to conduct research and practice within the framework of these values. | |||||||||||
Ders - Öğrenme Kazanımı İlişkisi
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Ability to use and apply current technical concepts and practices in the information technologies of software engineering, data management and computer security. | |
| 2) | Understanding user needs, analyzing them, and using them in the selection, evaluation, and management of computer-based systems. | |
| 3) | Ability to use data structures and develop algorithms. | |
| 4) | Ability to analyze and interpret complex big data systems. | |
| 5) | Ability to interpret and apply concepts and algorithms in machine learning. | |
| 6) | Understanding of the mathematical foundations of deep learning in the field of data analysis and the ability to apply the theory. | |
| 7) | Ability to solve complex data structures, develop and apply deep learning models, and interpret artificial intelligence-focused research on these topics. | |
| 8) | Ability to apply deep learning techniques and interpret real-world datasets and projects to solve problems in image analysis, natural language processing, and recommendation systems. | |
| 9) | Ability to transfer the basic principles and mathematical infrastructure of digital signal processing to practical applications. | |
| 10) | Gaining knowledge about the tools and technologies used via the Internet and the different technologies used for server coding languages and tools. | |
| 11) | Ability to understand of how genes function in multicellular species, the flow of genetic information in single-cell organisms, and the ability to interpret and apply biotechnology applications. | |
| 12) | Being aware of ethical values and understanding the need to conduct research and practice within the framework of these values. |
Öğrenme Etkinliği ve Öğretme Yöntemleri
| Bireysel çalışma ve ödevi | |
| Course | |
| Grup çalışması ve ödevi | |
| Okuma | |
| Homework | |
| Problem Çözme | |
| Proje Hazırlama |
Ölçme ve Değerlendirme Yöntemleri ve Kriterleri
| Yazılı Sınav (Açık uçlu sorular, çoktan seçmeli, doğru yanlış, eşleştirme, boşluk doldurma, sıralama) | |
| Homework | |
| Bireysel Proje | |
| Grup Projesi | |
| Sunum | |
| Raporlama |
Assessment & Grading
| Semester Requirements | Number of Activities | Level of Contribution |
| Homework Assignments | 1 | % 20 |
| Project | 1 | % 30 |
| Midterms | 1 | % 20 |
| Semester Final Exam | 1 | % 30 |
| total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 70 | |
| PERCENTAGE OF FINAL WORK | % 30 | |
| total | % 100 | |
İş Yükü ve AKTS Kredisi Hesaplaması
| Activities | Number of Activities | Duration (Hours) | Workload |
| Course Hours | 14 | 3 | 42 |
| Project | 1 | 50 | 50 |
| Homework Assignments | 1 | 70 | 70 |
| Midterms | 1 | 3 | 3 |
| Final | 1 | 3 | 3 |
| Total Workload | 168 | ||