Bilgisayar Mühendisliği (YL) (Tezli) (İngilizce)
Ders İçerikleri


1. Semester

Course CodeCourse Name(T+A+L)ECTSCompulsory/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 CodeCourse Name(T+A+L)ECTSCompulsory/Elective
IGE5500 Scientific Research Methods and Ethics 3+0+ 9 Compulsory

2. Semester

Course CodeCourse Name(T+A+L)ECTSCompulsory/Elective
COE5102 Seminar 0+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.
Course CodeCourse Name(T+A+L)ECTSCompulsory/Elective
COE5104 Advanced Database Systems 3+0+0 6 Compulsory
Course CodeCourse Name(T+A+L)ECTSCompulsory/Elective
COE5106 Advanced Algorithm Design 3+0+0 6 Compulsory

3. Semester

Course CodeCourse Name(T+A+L)ECTSCompulsory/Elective
COE5191 Tez Çalışması 1 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 CodeCourse Name(T+A+L)ECTSCompulsory/Elective
COE5192 Thesis Study 2 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 CodeCourse 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 CodeCourse 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 CodeCourse Name(T+A+L)ECTS
AO5007 Data Science 3+0+0 6
Course CodeCourse 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 CodeCourse 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 CodeCourse 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 CodeCourse 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 CodeCourse 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 CodeCourse Name(T+A+L)ECTS
COE5002 Combinatorial Optimization 3+0+0 6
The course starts with the techniques of linear programming to solve standard problems such as max-flow and shortest path, and then move onto topics of integer programming, NP-completeness and approximation algorithms. The student is expected to build and develop mathematical maturity throughout the course.
Course CodeCourse Name(T+A+L)ECTS
COE5003 Approximation Algorithms 3+0+0 6
The course starts with the basic concepts of combinatorial optimization and intractability and quickly moving onto the simplest forms of approximation techniques such as greedy and local search. Linear programming plays a central role in the course as it provides the basis for several approximation algorithms via rounding and the primal-dual schema. We will also review the basics of randomized algorithms and semi-definite programming.
Course CodeCourse Name(T+A+L)ECTS
COE5004 Computational Complexity 3+0+0 6
Course CodeCourse Name(T+A+L)ECTS
COE5005 Graph Theory 3+0+0 6
Course CodeCourse 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 CodeCourse Name(T+A+L)ECTS
COE5008 Advanced Digital System Design 3+0+0 6
While designing a sample processor, hardware programming language is taught using Verilog-HDL. Verification of designs is essential for complex systems. Each subunit is validated by simulation using Verilog-HDL before being integrated into the main system. Designs are synthesized and tested with programmable devices (FPGA) available from the market and software provided by manufacturers.
Course CodeCourse 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 CodeCourse Name(T+A+L)ECTS
COE5011 Advanced Object Oriented Programming 3+0+0 6
To introduce basic concepts of the object-oriented programming. To design software by using classes. To be able to use encapsulation, operator loading and inheritance while developing software. To know STL in order to implement software.
Course CodeCourse 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 CodeCourse Name(T+A+L)ECTS
COE5014 Soft Computing and Metaheuristics 3+0+0 6
Course CodeCourse Name(T+A+L)ECTS
COE5016 Computer Vision 3+0+0 6
Introduction to computer vision, Image formation, Modeling the image, Image acquisition patterns, Smoothing, Detail detection, Detail binding, Multi-scale approaches, Setting up a surface, From shading, Motion shape, Creating a time and image analysis and query model.
Course CodeCourse Name(T+A+L)ECTS
COE5018 Service Oriented Architectures 3+0+0 6
Course CodeCourse Name(T+A+L)ECTS
COE5020 Bioinformatic 3+0+0 6
Course CodeCourse Name(T+A+L)ECTS
COE5021 Machine Learning 3+0+0 6
This course provides a comprehensive overview of machine learning, covering both supervised and unsupervised learning approaches. It begins with an exploration of supervised learning, addressing regression problems and classification problems, including logistic regression, K-Nearest Neighbor, decision trees, handling imbalanced datasets, random forests, and techniques like cross-validation. The course delves into exploratory data analysis and data pre-processing techniques essential for effective machine learning. It then progresses to advanced topics such as hyperparameter tuning, dimensionality reduction, and unsupervised learning. Ensemble learning methods, particularly boosting techniques, are covered, along with an in-depth study of artificial neural networks, including perceptrons and multi-layer networks.
Course CodeCourse 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 CodeCourse Name(T+A+L)ECTS
COE5024 Advanced Software Engineering 3+0+0 6
Course CodeCourse Name(T+A+L)ECTS
COE5025 Distributed Systems 3+0+0 6
The content provided captures the essence of a course on Distributed Systems. It highlights key aspects such as the distribution of data over a network, the appearance of a single computer to system users, communication through message passing, and various themes including process distribution, data distribution, concurrency, resource sharing, synchronization, and more. It also emphasizes the importance of designing, implementing, and debugging large programming projects as part of the course. Overall, the content provides a good overview of the course's focus and objectives.
Course CodeCourse 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 CodeCourse Name(T+A+L)ECTS
COE5027 Natural Language Process 3+0+0 6
Regular Expressions, Text Normalization, Edit Distance, N-gram Language Models, Naive Bayes and Sentiment Classification, Vector Semantics and Embeddings, Sequence Labeling for Parts of Speech and Named Entities, Transformers and Pretrained Language Models, Machine Translation, Question Answering and Information Retrieval, Chatbots and Dialogue Systems, Automatic Speech Recognition and Text-to-Speech
Course CodeCourse 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 CodeCourse Name(T+A+L)ECTS
CYS5004 Advanced Cryptography 3+0+0 6
Course CodeCourse Name(T+A+L)ECTS
CYS5012 Cyber Securıty 3+0+0 6
Course CodeCourse Name(T+A+L)ECTS
CYS5013 Computer Network Securıty 3+0+0 6
Course CodeCourse 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.