INFORMATION TECHNOLOGIES (MASTER) (WITH THESIS) (ENGLISH)
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

Ders Genel Tanıtım Bilgileri

Course Code: 3024211551
Ders İsmi: Thesis Study
Ders Yarıyılı: Fall
Ders Kredileri:
Theoretical Practical Labs Credit ECTS
0 0 0 0 30
Language of instruction: EN
Ders Koşulu:
Ders İş Deneyimini Gerektiriyor mu?: No
Other Recommended Topics for the Course:
Type of course: Necessary
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 : Assoc. Prof. Esengül SALTÜRK
Course Lecturer(s):
Course Assistants:

Dersin Amaç ve İçeriği

Course Objectives: To carry out the thesis work, which is necessary for the student to receive a master's degree in engineering, under the supervision of the advisor.
Course Content: An independent study under the supervision of an advisor: Research on exploring a potential study area.
Identification of a specific problem from the selected study area. The results from this research study are written
in the thesis document and a possible academic paper, and presented in an oral presentation.

Learning Outcomes

The students who have succeeded in this course;
Learning Outcomes
1 - Knowledge
Theoretical - Conceptual
2 - Skills
Cognitive - Practical
3 - Competences
Communication and Social Competence
Learning Competence
1) It can provide the necessary knowledge to suggest and use appropriate algorithms and techniques.
2) It can deepen in engineering approaches acquired at the undergraduate level.
Field Specific Competence
Competence to Work Independently and Take Responsibility

Ders Akış Planı

Week Subject Related Preparation
1) Thesis preperation Articles on thesis topic
2) Thesis preperation Articles on thesis topic
3) Thesis preperation Articles on thesis topic
4) Thesis preperation Articles on thesis topic
5) Thesis preperation Articles on thesis topic
6) Thesis preperation Articles on thesis topic
7) Thesis preperation Articles on thesis topic
8) Thesis preperation Articles on thesis topic
9) Thesis preperation Articles on thesis topic
10) Thesis preperation Articles on thesis topic
11) Thesis preperation Articles on thesis topic
12) Thesis preperation Articles on thesis topic
13) Thesis preperation Articles on thesis topic
14) Thesis preperation Articles on thesis topic
15) Thesis preperation Articles on thesis topic
16) Thesis presentation Articles on thesis topic

Sources

Course Notes / Textbooks: Tez konusuyla ilgili makaleler
References: Articles on thesis topic

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 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. 5
2) Understanding user needs, analyzing them, and using them in the selection, evaluation, and management of computer-based systems. 5
3) Ability to use data structures and develop algorithms. 5
4) Ability to analyze and interpret complex big data systems. 5
5) Ability to interpret and apply concepts and algorithms in machine learning. 5
6) Understanding of the mathematical foundations of deep learning in the field of data analysis and the ability to apply the theory. 5
7) Ability to solve complex data structures, develop and apply deep learning models, and interpret artificial intelligence-focused research on these topics. 5
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. 5
9) Ability to transfer the basic principles and mathematical infrastructure of digital signal processing to practical applications. 4
10) Gaining knowledge about the tools and technologies used via the Internet and the different technologies used for server coding languages and tools. 5
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. 5
12) Being aware of ethical values and understanding the need to conduct research and practice within the framework of these values. 5

Öğrenme Etkinliği ve Öğretme Yöntemleri

Course
Okuma
Homework

Ölçme ve Değerlendirme Yöntemleri ve Kriterleri

Sözlü sınav
Sunum
Tez Sunma

Assessment & Grading

Semester Requirements Number of Activities Level of Contribution
Semester Final Exam 1 % 100
total % 100
PERCENTAGE OF SEMESTER WORK % 0
PERCENTAGE OF FINAL WORK % 100
total % 100

İş Yükü ve AKTS Kredisi Hesaplaması

Activities Number of Activities Duration (Hours) Workload
Study Hours Out of Class 1 900 900
Total Workload 900