General concepts about the course, collapse of the Ottoman Empire, leading to National Struggle, congresses, Sevres Treaty, wars, opening of Turkish Great National Assembly, Mudanya Armistice, Lausanne Treaty discussions.

Abolishment of sultanate and declaration of Republic, strategy and importance of Turkish Revolution, financial, social, legal, educational and cultural renovation, experiments for multi- party system, Turkish foreign policy during Atatürk?s term, basic principles of Atatürkism.

Introduction to computing concepts, introduction to the design and operation of digital computers, logic circuits, integrated circuits, processor organization, the relationship between hardware and software, Linux operating systems, C/C++ development on Linux.

Introduction to computer systems, hardware, software. Numerical data and base conversion. Introduction to basic network services. Algorithms and problem solving. Debugging approaches. C/C++ variables, data types and expressions, simple input / output commands. Arithmethic and logical operators. Branching control structures. Arrays. Loop structures. Functions. Structuresand union. Linked lists.

Functions in algorithms, reference variables as parameters, value and reference variables and memory usage, static and automatic variables. Multi-dimentional arrays, strings, vectors, dynamic arrays. File I/O. C pointer concept, pointer arithmetic. Recursion. Inheritence. OOP applications.

Computer abstraction and technology, computer performance, arithmetics for computers, instruction set architecture, MIPS Assembly Language, processor structure, datapath and control unit design, pipelined datapath and control, memory hierarchy, cache memory, virtual memory.

Introduction to digital systems; number systems; Boolean algebra; logic gates; gate level minimization; combinational logic design; combinational logic circuits; adders, comparators, encoders, decoders, multiplexers; Sequential logic circuits: Latches, flip flops, registers, counters; Sequential logic design: Finite state machines; simulation of logic circuits with Verilog HDL.

Computers and hardware, Algorithmic approach and the flexibility of C language features, Data structures and models, Algorithmic program design and flow charts, the program speed and memory requirement, Sorting algorithms, search algorithms, lists, linked lists, linked lists and applications, Stacks, Queues, the tree data model and its applications, Graf concept and data model, graph algorithms, data compression methods.

Basic concepts of electric circuits: current, voltage, ideal circuit elements, power and energy. Definition of measurement. Direct current measuring devices; current, voltage and resistance measurements. Electrical units; measurement methodes; measurement errors; Ohm's law; Kirchhoff's current and voltage laws and application of these laws in circuit analysis. Sinusoidal signals, average and effective (RMS) value. Oscilloscope. Electronic devices: Diode and rectifiers. MOSFET and amlifier circuit, CMOS logic gates.

Introduction to signals and systems, continuous time signals and systems, Laplace transform, Fourier series, Fourier transform, discrete time signals and systems, sampling theory, z-transform, discrete time Fourier transform, Matlab applications of continuous and discrete time signals and systems concepts.

This is an introductory course on the internal operations and fundamental principles of modern operating systems. Specifically, this course will cover core concepts such as processes and threads, deadlocks, memory management, and file systems.

General description, TCP/IP, FTP, WWW, HTTP, URL, What is HTML?, HTML pages general structure, text edit, using pictures, tables, links, What is JavaScript?, using JavaScript with HTML, JavaScript Syntax, variables, operators, functions, loops, arrays, time functions, communications with HTML forms, JavaScript document, window object models, DHTML, CSS, layers.

This course examines the architecture, operation, and interfacing of external devices connected to computer systems. Students will study the underlying principles of input/output mechanisms, storage technologies, display systems, and standard communication protocols such as USB, PCIe, I2C, and SPI. Through theoretical learning and practical applications, participants will understand how hardware peripherals interact with the central processing unit and operating system, enabling them to design and integrate efficient I/O subsystems.

The objective of this course is to teach the steps a computer engineer would participate in the development of a commercial product, from the idea stage all the way to the prototyping. Based on the idea of developing a mobile payment platform, students prepare, perform, and analyze market surveys; design and document system architecture, write code and perform system testing.

Introduction to Verilog HDL, gate-level modelling, data-flow level modelling, behavioral modelling. Simulation ofcombinational circuits using Verilog. Sequential circuit design, registers and shift registers, sequential circuit timing, synchronous design methods, sequential circuit design using Verilog. Asynchronous sequential circuits. Finite state machines design. Memory elements, field programmable gate arrays (FPGAs) register transfer level (RTL) design.

FPGA and CPLD architectures, FPGA programming technologies, FPGA logic cell, programmable connection and I/O cells, FPGA design flow, verification and synthesis, FPGA based verification and synthesis flow and implementation, datapath and control unit design, picoblaze processor, design examples.

Introduction to Microcontroller architecture and instruction set, assembly language and development platform applications, interrupts, I/O ports, timers, A/D converters and applications.

This course provides an in-depth introduction to the theory and engineering applications of fuzzy logic. Students will learn how to mathematically model uncertainty, imprecision, and human-like reasoning using fuzzy sets, membership functions, fuzzy rules, and fuzzy inference systems. Through practical assignments, participants will design and implement fuzzy controllers and decision-making algorithms, exploring their effectiveness in areas such as intelligent control, pattern recognition, and expert systems.

The objective of this course is to teach students the basic principles of computer networking, the protocols and applications used for two or more devices to communicate with each other. Students use Wireshark to observe the working of these protocols and applications during the lab exercises.

Mathematics foundation, problem solving strategies, proof techniques, complexity analysis, upper and lower bounds, sorting and searching, graph algorithms, divided-and-conquer, dynamic programming, greedy method, geometric algorithms, probabilistic algorithms.

The course introduces some fundamental concepts in automata theory and formal
languages including finite automata, regular expressions, regular languages, context free grammar and languages, pushdown automata, Turing machines.

History and philosophical foundations of AI, intelligent agents, problem solving by searching, informed search methods, propositional and predicate logic, building a knowledge base, planning, uncertainty and learning.

This course explores advanced problem-solving techniques in artificial intelligence used when traditional, exact methods are computationally infeasible. Students will study various heuristic and metaheuristic algorithms, including A* search, genetic algorithms, simulated annealing, and swarm intelligence. Through practical implementations, participants will learn how to design and apply these approximate methods to efficiently solve complex optimization, search, and routing problems.

Two dimension linear time-invariant signals and systems, two dimension convulation sum, digital FIR filter design, discrete Fourier and Cosine transformations, image restoration, image printing, coding techniques.

A general view of the definition of data mining, application fields, techniques and models. Data mining phases: Setting the goal, gathering data set aligning with the goal (choosing data set), sorting and preprocessing data, data reduction and data conversion, choosing the data mining learning algorithm, model evaluation and information presentation, commenting on the obtained information. Analyzing the data mining learning algorithm: decision trees, classification, finding correlations, k-neighbour algorithm, clustering algorithms.

Programming domains. Language evaluation criteria. Language categories. Programming environments. Minimal hardware programming: pseudocode. Functional programming: LISP. The first step toward sophistication: ALGOL 60. Programming based on Logic: Prolog. Historys largest design effort: Ada. Object-oriented programming: Smalltalk. Combining imperative and object-oriented features: C++. An imperative-based object oriented language: Java. Scripting languages: Javascript, PHP, and Python. Describing syntax and semantics. Lexical and syntax analysis. Names, binding, type checking and scopes. Data types. Expressions, assignment ,statements and subprograms. Abstract data types and encapsulation constructs. Functional programming languages. Logic programming languages.

Overview of Electronic Commerce, The Digital Economy, Business to Consumer ( B2C ) E-Commerce, Business to Business ( B2B ) E-Commerce, Other E-Commerce Models and Applications, Building E-Commerce Systems, Implementing E-Commerce.

This course is an introductory-level pattern recognition course. It aims to systematically cover key topics in pattern recognition, such as parameter estimation, Bayesian decision theory, linear bracket functions, and hidden Markov models, and to examine their applications to real-world problems.

Basic concepts, basic units in computer architecture, processors and processor types, CPU, memory, I/O and interconnection, interrupts, storage systems, case study, multiprocessor systems and parallelism, architecture for personal computers.

This course is an introductory machine learning course. It aims to provide a solid understanding of fundamental problems in the field of machine learning. The course will introduce several supervised and unsupervised machine learning algorithms and approaches, including linear regression, logistic regression, decision trees, neural networks, clustering, and dimensionality reduction.

Essential knowledge on embedded systems technology and techniques, embedded systems design methods, modeling of embedded systems, architecture of embedded CPUs, components for embedded programs, embedded program design and analysis, embedded and real-time operating systems, ARM based embedded system design and implementation.

Terminology and classifications of robotic, Representation of location; coordinate
systems, translation, rotation, movement, displacement, Fundamentals of robotic
manipulators; machine elements, joints, mechanisms, Modelling of robotic mechanism:
Kinematic modelling, Modelling of robotic mechanism: Invers kinematic modelling,
Environmental elements of robotic: Sensors and conditioners, Actuators and drivers,
Controller design: fundamentals, Controller design: embedded and soft electronic
controllers, Motion control, Fundamentals of robotic software: endless control loop,
Widely used robot programming languages, Developing robot programming languages,
Future advances in technology related with robotic.

This course introduces the fundamental concepts of mobile robotics, focusing on both hardware architecture and software development. Students will explore core topics such as kinematics, sensor integration, localization, mapping, and autonomous navigation algorithms. Through hands-on projects, participants will gain practical experience in designing, programming, and testing mobile robotic systems for real-world applications.

- To provide students with a comprehensive understanding of technical SEO principles and practices. - To equip students with the necessary skills and knowledge to perform advanced technical SEO analyses and optimizations. - To empower students to effectively implement technical SEO strategies to improve website visibility and search engine rankings. - To explore the latest trends and developments in technical SEO and examine their impact on digital marketing strategies. - To empower students to analyze and solve complex technical SEO problems in real-world scenarios.

The primary goal of this course is to provide students with a fundamental understanding of the Internet of Things (IoT) concept, its components, technologies, and applications. Students will learn to design, develop, and analyze IoT systems to solve real-world problems, focusing on data analytics and security.

Definition of Neural Network, models of a neuron, network architectures, perceptron, knowledge representation. Multilayer perceptrons, supervised learning, unsupervised learning, hybrid learning, kernel methods and radial basis function networks, k-means clustering, hybrid learning procedure for RBF networks, Support Vector Machines, Machine Learning.

This course covers the fundamental techniques, architectures of mobile and wireless networks.

The course is intended to lay the ground and prepare students for their work in ?CEN 491 Graduation Project?. Students are expected to choose a graduation project topic proposed and presented by the instructors, conduct a feasibility study, document their findings and presentation them at the end of the course. Successful students will be required to continue their project work in the subsequent course with the same advisor on the selected topic.

Graduation project is the culminating design and development experience that allow students to demonstrate their ability to provide a solution using all the assets gathered during the computer engineering program. Project is expected to address a current problem in the fields that are covered or related to computer engineering. The focus will be on the design and development of finished product documented according to existing industrial standards.

Work during the internship is defined by the company.

This course aims to improve students' expository reading and writing skills. Structural elements of a text, collocations, connectors, sentence heads and phrases for more effective expression are among the subjects to be studied. Texts that are related to the field are selected to be used as course material to motivate and involve students. This course also introduces students with basic terminology in their fields.

This course aims to improve students? analytical reading and writing skills. Texts that are related to the field of computer engineering and software engineering are selected to be used as course material to motivate and involve students. Students read and analyze texts and produce their own texts that involve their responses to the input they receive. This course also introduces students with basic terminology in their fields.

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OHS regulations; hazards in workplace environment and preventation form thesedangers; risk assement; personal protective equipment.

Definition and types of functions, drawing their graphics, limits and continuity, definition of derivative and geometric meaning, differentiation rules, integration, definite and indefinite integrals.

Sequences and infinite series, parametrizations of plane curves, polar coordinates, vectors and the geometry of space, functions of several variables, limits and continuity in higher dimensions, partial derivatives, directional derivatives and gradient vectors, extreme
values and saddle points, Lagrange multipliers, multiple integrals

System of linear equations; matrices and algebraic operations of matrices; methods for solving linear equation systems; Gauss and Gauss-Jordan methods; determinants and properties of determinants; Cramer's
rule; vector spaces and subspaces; basis, dimension and rank; eigenvalues and eigenvectors; diagonalization.

Modeling techniques for engineering problems, introduction to programming and software algorithms, techniques for finding roots of equations, solution of lineer equation systems and matrix algebra, curve fitting, least squares regression, interpolation, techniques for numerival integration, solution techniques of bundary and initial value problems and their engineering applications.

Inıtial-boundary value problems, first-order differential equations; exact differential equations and integrating factor, linear equations, Bernoulli equations, homogen equations, higher order linear differential equations, constant-coefficient linear differential equations, undetermined coefficients method, variation of parameters, Cauchy-Euler differential equations, Laplace transforms and solving linear equtions with Laplace transforms, series
solutions of second order linear differential equations.

Statistics, data, variable, frequency distributions, graphics, measures of central tendencies, measures of central dispersions, probability: The axioms of probability, random variables, Discrete random variables,
continuous random variables, probability distributions: Discrete probability distributions, continuous probability distributions, statistical decision theory, estimation.

The main goal is understanding of the fundamental discrete mathematics topics, discrete structures and enhancing the analytical problem solving capabilities is the important purpose.

Physics and measurement; physical quantities; motion in one dimension; vectors; motion in two dimensions; Newton's laws of motion; work and kinetic energy; potential energy and conservation of mechanical energy; linear momentum, conservation of linear momentum; collisions; dynamics of a system of particles; rotational dynamics; torque and angular momentum; equilibrium of rigid bodies.

Charge and Matter, Coulomb?s Law, Electric Field, Gauss?s Law, Electrostatic Potential, Capacitance and Dielectrics, Current and Resistance, Electromotive Force and Circuits, The Magnetic Field, Sources of the Magnetic Field, Faraday?s Law, Inductance, Alternating Current Circuits, Electromagnetic Waves and Maxwell?s Equations, Electromagnetic Spectrum.

In this course, students will learn basic principles of object oriented programming using C++ programming language.

The aim of the course is to teach the basic principles of database management systems. Requirements, analysis, design, implementation and maintenance phases which are being the fundamental topics of database engineering. Entity relationship modeling, normalization types, relational algebra are described within the database development approach. Students will learn how to develop a database in an organized step-by-step fashion. If time permits students will develop a database design project.

The scope of software engineering, software development life cycle models, software process, software teams, software tools, software testing, objects and modules, requirements, object-oriented analysis. Types of design and object-oriented design, methods of bringing together modules/objects during the implementation process, post-delivery maintenance of the software product, UML diagrams.

In this course, the studies will be made about the understanding, explanation, reading and writing, various features of written explanation will be considered and examined with critical viewpoint. The punctuation marks which are the basis of the written explanation and writing rules will be made evident and the correct use of such rules for efficient and sound expression will be elicited.

In this course, the studies will be made about the understanding, explanation, reading and writing, various features of written explanation will be considered and examined with critical viewpoint. The punctuation marks which are the basis of written explanation and writing rules will be made evident and the correct use of such rules for efficient and sound expression will be elicited.