Algebraic structures, groups, number systems, prime numbers, mathematical induction, permutation and combination, recursions, graph theory and applications.

Mathematical Fundamentals, basic cryptologic techniques, crypto analysis, elliptic curve cryptology, quantum cryptology.

Introduction to data structures and programming, pointers and arrays, dynamic memory management, object oriented design, linked lists, stacks, queues, Recursion as a Problem-Solving Technique, trees, binary search trees, sets, maps, heaps, priority queues, graphs.

Communication between processes, synchronization and election. Distributed negotiation, transaction and copied data. Introduction to parallel and distributed computing systems and basic concepts. Synchronization mechanisms. Deadlocks. Basics of distributed operating systems, Unix based multi-processing operating systems, semaphores, ADA contacts, transporters, Multi processors and task planning for distributed database systems.

Computer architecture performance, command set design, pipeline, command level similarity, memory systems, cache design and analysis, storage systems, inter-connected networks, multi processors architecture and embedded systems.

Basic concepts and CMMI, software verification and validation, software configuration management, software quality assurance process, software documentation process, software management processes.

Mathematical model of an image. The frequency concept in an image and its frequency spectrum. Sampling of an image and conditions on sampling frequency. Separability in 2-D signals. Expansion of an image into Fourier series. The 2-D Fourier transform, The Fourier transform of separable images. The z-transform and transfer function. The linear operations applied to an image. Image segmentation. Image restoration. Image compression.

Internet protocols, interior routing protocols, shortest open path algorithm, exterior routing protocols, multiprotocol label switching, IP multicast, ad-hoc internet structures, QoS routing.

Backup networks, wireless communication, GSM, GPRS,UMTS, wireless local area networks, mobile IP ,WAP.

Introduction to network security, problems, DES, 3DES, AES, RSA, diffie-hellman, MD-5, SHA-1, digital signatures, network security standards. Secure electronic mail (PGP), S-MIME, SSL, TLS, IPSec.

Study and presentation of current research topics in computer engineering

Study and presentation of current research topics in computer engineering

This course introduces students to the fundamentals of digital forensics technology. Emphasis is placed on identifying cyber threats to, and vulnerabilities of, computer systems and how to minimize them. Students will learn how hackers identify victims, how attacks are executed, and various methods used to access to computer systems

Machine Learning is a sub-discipline of AI and mainly deals with the development of learning methods. These methods are used to solve a wide variety of problems. Currently, deep learning is very important, but at the same time statistical learning methods still receive great attention. Machine learning methods are used where people cannot yet define open solution methods. When data is noisy and incomplete, this also affects real world issues. For example, it is not difficult to recognize a particular object even under changing lighting conditions or when partially covered by another object. For computers, this is still a huge challenge. Machine Learning helps them overcome these challenges.

Meaning, importance and aim of the research. Types of the research. Defining the research problem and approaches to the problems and their solutions. Design of the research. Sampling design. Measurement techniques. Data collecting techniques. Analysis of data. Presentation and preparation of research document. Ethic.

Students are required to give a seminar on the subject that they are planning to do research.

Foundation of parallel and distributed processing, abstract models for parallel computing, PRAM, analysis of distributed and parallel algorithms and their complexity, design techniques of distributed and parallel algorithms, parallel searching and sorting algorithms, distributed searching, parallel numerical and geometric algorithms, graph algorithms, distributed networks.

Turing machines, computation models. Finite state models. Computational complexity. Sequential functions. Computability and insolvability. Speed-up and layering theorems.

Introduction to artificial intelligence, problem solving, search, intuitivity, planning, expert systems, nerve networks, robotic applications, natural language processing, LISP, PROLOG and artificial intelligence applications.

Dynamic programming, matrix hypothesis and modeling with matrices, variable transformation and its applications at multivariate functions, non-linear and linear model proposition and its applications, stochastic models, introduction to Markov chains, queue models and its applications, introduction to multivariate statistical analysis, simulation models and its applications, other chosen modeling subjects.

Introduction, imaging systems, image processing techniques, image restoration, noise removal, segmentation, edge detection algorithms, corner detection algorithms, motion detection and motion tracking, feature detection and matching, camera calibration, geometric transformations, parameter estimation and RANSAC algorithm, stereo vision, object recognition.

Conduct and present a research investigation or an experimental research investigation .

The qualifying exam consists of two steps, written and oral. In order to continue the program, students must successfully pass on both stages.

Students who are that are succesful at the qualifying exam, propose their thesis that are suitable to scientific and ethical.

Thesis study and thesis composition of the Computer Engineering doctorate students with the consultancy of the faculty.

This course introduces students to the fundamentals of digital forensics technology. Emphasis is placed on identifying cyber threats to, and vulnerabilities of, computer systems and how to minimize them. Students will learn how hackers identify victims, how attacks are executed, and various methods used to access to computer systems

Makine Öğrenmesi, YZ'nın bir alt disiplinidir ve temel olarak öğrenme yöntemlerinin geliştirilmesiyle ilgilenir. Bu yöntemler çok çeşitli problemleri çözmek için kullanılır. Şu anda, derin öğrenme çok önemlidir, ancak aynı zamanda istatistiksel öğrenme yöntemleri (derin öğrenmeden önceki eğilim) hala büyük ilgi görmektedir. Makine öğrenmesi yöntemleri, insanların açık çözüm yöntemlerini henüz tanımlayamadığı yerlerde kullanılır. Veriler gürültülü ve eksik olduğunda, bu aynı zamanda gerçek dünya meselelerini de etkiler. Örneğin, değişen aydınlatma koşullarında veya kısmen başka bir nesne tarafından önü kapansa bile belirli bir nesneyi tanımak zor değildir. Bilgisayarlar için bu hala büyük bir zorluktur. Makine Öğrenmesi, bu zorlukları aşmalarına yardımcı olur.

Radar equation and system parameters, components of radar system, RCS and target characteristics, continuous wave radar, frequency modulated CW radar, MTI and pulsed Doppler radar, radar signal detection, wave forms, radar ambiguity function, radar measurements and applications.

Meaning, importance and aim of the research. Types of the research. Defining the research problem and approaches to the problems and their solutions. Design of the research. Sampling design. Measurement techniques. Data collecting techniques. Analysis of data. Presentation and preparation of research document. Ethic.

Main concepts of supply chain management and logistic systems, usage of mathematical models and quantitative techniques to analyze these systems, examination of basic components of supply chain (purchasing, inventory, production, transportation, etc?)

Theory and application of advanced engineering economy, equivalence, finance, financial definitions and analysis, project evaluation, profits and behaviors under risk, stochastic analysis.