INFORMATION TECHNOLOGIES (MASTER) (WITH THESIS) (ENGLISH)
Qualification Awarded Program Süresi Toplam Kredi (AKTS) Öğretim Şekli Yeterliliğin Düzeyi ve Öğrenme Alanı
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

Ders Genel Tanıtım Bilgileri

Course Code: 3000003002
Ders İsmi: Computer Networks
Ders Yarıyılı: Fall
Ders Kredileri:
Theoretical Practical Labs Credit ECTS
3 0 0 3 9
Language of instruction: EN
Ders Koşulu:
Ders İş Deneyimini Gerektiriyor mu?: No
Other Recommended Topics for the Course:
Type of course: Uzmanlık Alanı Zorunlu
Course Level:
Master TR-NQF-HE:7. Master`s Degree QF-EHEA:Second Cycle EQF-LLL:7. Master`s Degree
Mode of Delivery: Face to face
Course Coordinator : Prof. Dr. Naim Mahmood Musleh AJLOUNI
Course Lecturer(s):
Course Assistants:

Dersin Amaç ve İçeriği

Course Objectives: This is a graduate-level introductory course in Computer Networks. It makes a broad introduction to many concepts and algorithms, theory ve practical work in Computer Networks. The basic concepts and selected topics in computer networks and applications are presented.
Course Content: An overview of OSI and TCP/IP models and Internet architecture. Packet switching and circuit switching network technologies. Delay, loss and throughput in packet-switched networks. Analyzing network packets using a network analyzer program and network designs in a network modeling and simulation environment. Application Layer: Introduction to socket programming, application layer protocols: DNS, HTTP, FTP, SMTP, POP3, and peer-to-peer networking. Transport Layer: Principles of reliable data transfer, TCP and UDP protocols, flow control and congestion control. Network Layer: IP protocol and addressing. Routing Algorithms: Link State, Distance Vector, Hierarchical Routing, Routing in the Internet: RIP, OSPF, BGP protocols. Broadcast and multicast routing. Data link protocols, and local area networks: Ethernet and IEEE 802.11.

Learning Outcomes

The students who have succeeded in this course;
Learning Outcomes
1 - Knowledge
Theoretical - Conceptual
1) Bilgisayar ağlarındaki temel yöntemler hakkında bilgi.
2 - Skills
Cognitive - Practical
1) Ability to use knowledge to formulate, and solve practical problems using computer networking techniques.
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) An overview of OSI and TCP/IP models and Internet architecture. Computer Networking: A Top-Down Approach, 8th Edition, J. F. Kurose, K. W. Ross, Addison Wesley, 2020.
2) Packet switching and circuit switching network technologies. Computer Networking: A Top-Down Approach, 8th Edition, J. F. Kurose, K. W. Ross, Addison Wesley, 2020.
3) Delay, loss and throughput in packet-switched networks. Computer Networking: A Top-Down Approach, 8th Edition, J. F. Kurose, K. W. Ross, Addison Wesley, 2020.
4) Application Layer1: Socket programming, application layer protocols: DNS, HTTP, Computer Networking: A Top-Down Approach, 8th Edition, J. F. Kurose, K. W. Ross, Addison Wesley, 2020.
5) Application Layer 2: FTP, SMTP, POP3, and peer-to-peer networking. Computer Networking: A Top-Down Approach, 8th Edition, J. F. Kurose, K. W. Ross, Addison Wesley, 2020.
6) Transport Layer 1: Principles of reliable data transfer, Computer Networking: A Top-Down Approach, 8th Edition, J. F. Kurose, K. W. Ross, Addison Wesley, 2020.
7) Transport Layer 2: TCP and UDP protocols, flow control and congestion control. Computer Networking: A Top-Down Approach, 8th Edition, J. F. Kurose, K. W. Ross, Addison Wesley, 2020.
8) Midterm Exam
9) Network Layer 1: IP protocol and addressing. Computer Networking: A Top-Down Approach, 8th Edition, J. F. Kurose, K. W. Ross, Addison Wesley, 2020.
10) Network Layer 2: Routing Algorithms: Link State, Distance Vector, Hierarchical Routing, Computer Networking: A Top-Down Approach, 8th Edition, J. F. Kurose, K. W. Ross, Addison Wesley, 2020.
11) Network Layer 3: Routing in the Internet: RIP, OSPF, BGP protocols. Broadcast and multicast routing. Computer Networking: A Top-Down Approach, 8th Edition, J. F. Kurose, K. W. Ross, Addison Wesley, 2020.
12) Data Link Layer 1: Data link protocols Computer Networking: A Top-Down Approach, 8th Edition, J. F. Kurose, K. W. Ross, Addison Wesley, 2020.
13) Data Link Layer 2: Local area networks: Ethernet and IEEE 802.11. Computer Networking: A Top-Down Approach, 8th Edition, J. F. Kurose, K. W. Ross, Addison Wesley, 2020.
14) Project Demonstration / Midterm Exam II

Sources

Course Notes / Textbooks: Computer Networking: A Top-Down Approach, 8th Edition, J. F. Kurose, K. W. Ross, Addison Wesley, 2020.
http://gaia.cs.umass.edu/kurose_ross/online_lectures.htm
References: Computer Networking: A Top-Down Approach, 8th Edition, J. F. Kurose, K. W. Ross, Addison Wesley, 2020.
http://gaia.cs.umass.edu/kurose_ross/online_lectures.htm

Ders - Program Öğrenme Kazanım İlişkisi

Ders Öğrenme Kazanımları

1

2

Program Outcomes
1) Ability to use and apply current technical concepts and practices in the information technologies of 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 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
Okuma
Homework
Problem Çözme
Proje Hazırlama
Rapor Yazma

Ölçme ve Değerlendirme Yöntemleri ve Kriterleri

Yazılı Sınav (Açık uçlu sorular, çoktan seçmeli, doğru yanlış, eşleştirme, boşluk doldurma, sıralama)
Homework
Uygulama
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
Presentations / Seminar 1 70 70
Project 1 70 70
Homework Assignments 1 70 70
Midterms 1 3 3
Final 1 3 3
Total Workload 258