Bilgisayar Mühendisliği (YL) (Tezli) (İngilizce)
Ders İçerikleri
1. Semester
| Course Code | Course Name | (T+A+L) | ECTS | Compulsory/Elective |
| COE5101 | Bilimsel Araştırma Yöntemleri ve Etik | 3+0+0 | 9 | Compulsory |
| Knowledge and understanding Understand different scientific research designs and methods Learn how to set up a research study Understand correct ways to refer to and cite from scientific literature Skills and ability Discuss and explain differences between different research methods Perform literature reviews and reference relevant scientific literature Formulate a research proposal Assessment ability and attitudes Critically assess different research designs Analyse, set as contrast, compare and review scientific literature Discuss own view in relation to the published research | ||||
| Course Code | Course Name | (T+A+L) | ECTS | Compulsory/Elective |
| COE5105 | Scientific Writing Techniques | 2+0+ | 9 | Compulsory |
| This course is designed to enhance students' scientific writing skills, emphasizing effective communication of research findings and ideas in various contexts. Through a combination of theoretical discussions, practical exercises, and real-world applications, students will develop proficiency in crafting clear, concise, and impactful scientific documents. Topics covered include audience analysis, precision in language, clarity, energy in writing, organizational strategies, adaptation of style, and efficient time management. The course will also address specific formats such as emails, instructions, and proposals, providing students with a well-rounded skill set applicable to diverse scientific communication scenarios. | ||||
2. Semester
| Course Code | Course Name | (T+A+L) | ECTS | Compulsory/Elective |
| COE5102 | Graduate Seminar | 2+0+0 | 12 | Compulsory |
| A study aimed at improving students' ability to explain, interpret, discuss and communicate in front of the public, by preparing an original study that will contribute to current, academic and social developments in the field of education and/or thesis topics, in accordance with scientific research norms. | ||||
3. Semester
| Course Code | Course Name | (T+A+L) | ECTS | Compulsory/Elective |
| COE5191 | Master's Thesis | 0+0+0 | 30 | Compulsory |
| Literature review and research on the identified topic is performed under the supervision of the student’s thesis advisor. | ||||
4. Semester
| Course Code | Course Name | (T+A+L) | ECTS | Compulsory/Elective |
| COE5191 | Master's Thesis | 0+0+0 | 30 | Compulsory |
| Literature review and research on the identified topic is performed under the supervision of the student’s thesis advisor. | ||||
Department/Programme Elective Courses
| Course Code | Course Name | (T+A+L) | ECTS |
| AO5006 | Aspects of Deep Learning | 3+0+0 | 6 |
| Students must do projects using Python. Projects will be done on a team basis. | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| AO5007 | Data Science | 3+0+0 | 6 |
| Course Code | Course Name | (T+A+L) | ECTS |
| AO5012 | Human-Computer Interaction | 3+0+0 | 6 |
| Teaching the basic principles of user interfaces. Introduce students to usability models and principles. Get students to carry out user and task analyses. Teach design, prototype development, and evaluation by having students complete term projects. Discuss the effects of interface properties such as color and typography. Teach new user interface techniques. | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| AO5013 | Robotic Systems | 3+0+0 | 6 |
| In this course, sub-systems and components of autonomous robots are introduced, motion techniques are taught, applications related to trajectory planning are studied, control strategies for robots are explained, students are informed about new technologies and application areas in robots. | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| AO5017 | Computational Biology | 3+0+0 | 6 |
| The course includes basic concepts of genetics and genomics, next generation sequencing technologies, DNA sequencing, RNA sequencing, basic biology/bioinformatics databases and datasets, basic bioinformatics tools necessary for processing biological data, biological networks and creating and processing biological networks. | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| AO5018 | Machine Learning Operations | 3+0+0 | 6 |
| After completing this course satisfactorily, a student will: 1. Design a well-defined problem formulation for a basic MLOps problem. 2. Solve well-defined problems using MLOps methods and algorithms. 3. Explain basic concepts of MLOps methods. 4. Develop MLOps systems by programming languages. 5. Work as a team in a MLOps project. | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| COE5006 | Error-Correcting Codes | 3+0+0 | 6 |
| Linear codes, weights and distances, generator and control matrices, dual codes, Hamming codes, Reed Muller codes, Golay codes, bounds, finite fields, cyclic codes, BCH and Reed Solomon codes, weight distributions. | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| COE5012 | Parallel Computing | 3+0+0 | 6 |
| Parallel computing methods, algorithms and parallel architectures. Demonstration of parallel programming languages developed for different architectures on sample applications. Performance measurement and analysis of parallel programs. | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| COE5022 | Internet of Things | 3+0+0 | 6 |
| The course content covers the following basic topics: Basic Electronics and Hardware Information: Programming Languages: Internet of Things Protocols: Data Collection and Processing: Wireless Communication Technologies: Application Development and Platforms: Security and Privacy: Industrial IoT and Applications: | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| COE5026 | Wireless Adhoc Networks | 3+0+0 | 6 |
| The course Wireless Ad Hoc Networks will set off on an in-depth walk through the realm of wireless communication. The course will begin with the fundamental principles and challenges of ad hoc networks, routing algorithms, transport protocols, wireless internet, and network security. Insights into Quality of Service (QoS) considerations and energy management solutions in ad hoc networks will be offered as the course proceeds. Vehicular ad hoc networks a cutting-edge technology will also be discussed in the course. | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| CYS5004 | Advanced Cryptography | 3+0+0 | 6 |
| Course Code | Course Name | (T+A+L) | ECTS |
| CYS5012 | Cyber Securıty | 3+0+0 | 6 |
| Course Code | Course Name | (T+A+L) | ECTS |
| CYS5013 | Computer Network Securıty | 3+0+0 | 6 |
| Course Code | Course Name | (T+A+L) | ECTS |
| DATS5027 | Machine Learning Applications in Business | 1+2+ | 6 |
| 1. Overview of machine learning concepts and algorithms in a business context. 2. Data preprocessing, feature engineering, and data visualization techniques. 3. Supervised learning models for regression and classification in business decision-making. 4. Unsupervised learning for customer segmentation, market basket analysis, and anomaly detection. 5. Evaluation of machine learning models and deployment strategies for business applications. | |||