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 |
IGE5500 | Scientific Research Methods and Ethics | 3+0+ | 9 | Compulsory |
Course Code | Course Name | (T+A+L) | ECTS | Compulsory/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 Code | Course Name | (T+A+L) | ECTS | Compulsory/Elective |
COE5104 | Advanced Database Systems | 3+0+0 | 6 | Compulsory |
Course Code | Course Name | (T+A+L) | ECTS | Compulsory/Elective |
COE5106 | Advanced Algorithm Design | 3+0+0 | 6 | Compulsory |
Course Code | Course Name | (T+A+L) | ECTS | Compulsory/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. |
Course Code | Course Name | (T+A+L) | ECTS | Compulsory/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. |
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 |
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 Code | Course 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 Code | Course Name | (T+A+L) | ECTS |
COE5004 | Computational Complexity | 3+0+0 | 6 |
Course Code | Course Name | (T+A+L) | ECTS |
COE5005 | Graph Theory | 3+0+0 | 6 |
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 |
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 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 |
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 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 |
COE5014 | Soft Computing and Metaheuristics | 3+0+0 | 6 |
Course Code | Course 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 Code | Course Name | (T+A+L) | ECTS |
COE5018 | Service Oriented Architectures | 3+0+0 | 6 |
Course Code | Course Name | (T+A+L) | ECTS |
COE5020 | Bioinformatic | 3+0+0 | 6 |
Course Code | Course 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 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 |
COE5024 | Advanced Software Engineering | 3+0+0 | 6 |
Course Code | Course 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 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 |
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 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 |
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. |