Bilgisayar Mühendisliği (YL) (Tezsiz) (İngilizce)
Ders İçerikleri
1. Semester
| Course Code | Course Name | (T+A+L) | ECTS | Compulsory/Elective |
| COE5107 | Cloud Computing | 3+0+0 | 9 | Compulsory |
| Fundamental concepts and technologies in Cloud computing, Cloud computing tools and applications; benefits and challenges associated with Cloud computing. | ||||
| Course Code | Course Name | (T+A+L) | ECTS | Compulsory/Elective |
| COE5109 | Computer Architecture | 3+0+0 | 4 | Compulsory |
| Course Code | Course Name | (T+A+L) | ECTS | Compulsory/Elective |
| IGE5500 | Scientific Research Methods and Ethics | 3+0+ | 9 | Compulsory |
| 1. Introduction to Research Methods - Overview of scientific research and its importance - Types of research: qualitative, quantitative, and mixed methods 2. Critical Literature Review - Techniques for conducting systematic and critical reviews - Identifying research gaps and formulating research questions 3. Research Philosophy and Paradigms - Understanding positivism, interpretivism, and other paradigms - Aligning research design with philosophical foundations 4. Research Design and Methodology - Developing coherent research frameworks - Selecting appropriate methodologies for research objectives 5. Ethics in Research - Ethical considerations in research design and execution - Gaining access to data and maintaining confidentiality 6. Data Collection Techniques - Sampling strategies: probability and non-probability - Primary data collection: interviews, questionnaires, and diaries - Secondary data evaluation and analysis 7. Data Analysis Methods - Quantitative analysis: statistical tools and techniques - Qualitative analysis: thematic, narrative, and content analysis 8. Writing and Presenting Research - Structuring academic and consultancy reports - Effective presentation techniques for research findings 9. Practical Applications and Case Studies - Hands-on exercises in designing and conducting research - Real-world examples of ethical dilemmas and solutions 10. Final Project - Development of a mini research proposal - Peer presentations and constructive feedback sessions | ||||
2. Semester
| Course Code | Course Name | (T+A+L) | ECTS | Compulsory/Elective |
| COE5103 | Final Project | 0+0+ | 6 | Compulsory |
| Literature review and project study on a specific topic is performed under supervision. | ||||
| Course Code | Course Name | (T+A+L) | ECTS | Compulsory/Elective |
| COE5104 | Advanced Database Systems | 3+0+0 | 6 | Compulsory |
| This course covers Advanced Database System's concepts, Database Architecture and Storage systems. Advanced Query Processing and transaction managements are the other topics of this course. Concurrency control, recovery and fault tolerance techniques are the other topics of this course. The students will also learn about Distributed Databases and NoSQL concepts and techniques. Database security, data base privacy and their challenges are the other topics in this course. | ||||
| Course Code | Course Name | (T+A+L) | ECTS | Compulsory/Elective |
| COE5106 | Advanced Algorithm Design | 3+0+0 | 6 | Compulsory |
| Algorithms and data structures are fundamental to the discipline of computer science. This course introduces students to advanced techniques for designing, analyzing, and evaluating algorithms across a wide range of applications. Topics include algorithm efficiency analysis, brute force and exhaustive search methods, decrease-and-conquer and divide-and-conquer strategies, transform-and-conquer techniques, dynamic programming, greedy algorithms, and iterative improvement methods. Through both theoretical concepts and practical examples, students will develop a deeper understanding of algorithmic problem-solving approaches. | ||||
| Course Code | Course Name | (T+A+L) | ECTS | Compulsory/Elective |
| COE5108 | Computer Network Security | 3+0+0 | 6 | Compulsory |
| 1. Introduce to Basics of Computer Network Security: confidentiality, integrity and authentication, active and passive attacks, basic security protection mechanisms. 2. Networks and Security Metrics: entry point attacks, secure infrastructure, trust and threat model, Shannon’s secrecy, complexity theory, semantic security, and pseudorandom generators. 3. Symmetric-key Cryptographic Systems: Linear feedback shift register based pseudorandom generation, stream ciphers and block ciphers, encryption models, chosen plaintext/ciphertext attack (CPA), secure hash functions, MAC, authenticated encryption, correlation attacks and time-memory trade-off attacks. 4. Public-key Systems: security of public-key cryptography, basic schemes, digital signature, ECC, pairing-based IBC, fully homomorphic encryption, post-quantum digital signature, and fault attacks. 5. Network and Wireless Security: the man-in-the-middle attacks, mutual authentication and key establishment, cipher suite negotiation, network security protocols (IPsec, TLS/SSL, VPN), and attacks on TLS, radio air link protection (4G-LTE, 5G), IEEE 802.11 security solutions (flowed WEP, CCMP), jamming and relay attacks. 6. Broadcast and Multicast Security: multicast key distribution, hash chain, broadcast message authentication, Merkle tree based authentication and commitment. 7. Trusted Platform and System Security: trusted platform, temple response hardware, secure storage, remote attestation, anonymous authentication, and physical layer security. | ||||
Department/Programme Elective Courses
| Course Code | Course Name | (T+A+L) | ECTS |
| AO5001 | Artificial Intelligence Principles | 3+0+0 | 9 |
| Akıllı yazılım aracıları ve çok aracılı sistemlerin tasarımı, uygulanması ve seçilmiş uygulamaları. Akıllı davranışın hesaplamalı modelleri, problem çözme, bilgi temsili, akıl yürütme, planlama, karar verme, öğrenme, algılama, eylem, iletişim ve etkileşimi içerir. | |||
| 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 |
| AO5015 | Optimization Algorithms | 3+0+0 | 6 |
| The content of the course includes the concept of optimization and its uses, the development processes of metaheuristic algorithms, detailed information about the most commonly used algorithms and application examples. | |||
| 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 |
| COE5010 | Advanced Computer Architecture | 3+0+0 | 6 |
| Basic principles of computer architecture. Design and organization of computer architecture. Running of programs written with high level languages on computer hardware. Using of SPIM simulator. Interrupts, ISA and performance metrics. Single cycle data path, pipeline, pipelined data path and forwarding. Pipeline stallings and Intel Asm. SSE, MMX, caches, virtual memories, parallel programs and OpenMP. I/O, shared memories and instruction level parallelism. Scheduling. | |||
| 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 |
| COE5015 | Cloud Computing | 3+0+0 | 6 |
| This course will be presented using the “Essentials of CLOUD COMPUTING” book and also several survey papers published by Dr.Mohammad Masdari himself. In addition, this course presents the main challenges in cloud computing and is essential for the students who want to continue their master's degree in this context. | |||
| 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 |
| CYS5003 | Introductıon To Cryptography And Securıty Protocols | 3+0+0 | 6 |
| General concepts of cryptography, classical cryptosystem and basics of cryptanalysis, symmetric and asymmetric cryptography algorithms (OTP, DES, 3DES, AES, RC5, RSA), public key cryptography, cryptographic hash functions, data integrity and message authentication, digital signatures, secure key exchange (Diffie–Hellman key exchange), authentication mechanisms , authentication protocols, security protocol design, analysis and verification, access control and authorization. Some existing application layer security protocols (such as email security) . | |||
| 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 |
| DATS5008 | Business Intelligence and Data Visualization | 1+2+0 | 6 |
| 1. Introduction to Decision Support Systems: key concepts and components 2. Decision Making Process: types of decisions, decision making under uncertainty 3. Decision Support System Modeling: linear, nonlinear, and discrete models 4. Data Warehousing and Data Mining for Decision Support: ETL processes and techniques 5. Advanced Topics in DSS: machine learning, artificial intelligence, and real-time decision systems | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| DATS5022 | Sustainability and Data Science | 1+2+0 | 6 |
| 1. Introduction to Sustainability and its Intersections with Data Science 2. Fundamentals of Environmental Data and Metrics 3. Sustainable Practices in Data Collection and Analysis 4. Case Studies on Big Data for Sustainability 5. Implementing AI and Machine Learning for Sustainable Solutions | |||
| 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. | |||