LİSANS DERS İÇERİKLERİ

\ Eğitim

**CONTENT OF THE COURSES AND THE RECOMENDED BOOKS**

**I. SEMESTER**

**Physics I**

Standards and units; vectors and coordinate systems; kinematics; dynamics; work, energy and power; conservation of energy; dynamics of system of particles; collisions; rotational kinematics and dynamics; equilibrium of rigid bodies; oscillations; gravitation.

**Textbook****:**

Physics for Engineers and Scientist, Fishbane, Gasiorowıcz, Prentice Hall.

Bueche, F.L. D. Jerde, Fizik İlkeleri, Palme Yayıncılık, (Çev: Kemal Çolakoğlu).

**Chemistry**

Develop an understanding of atomic and molecular structure of matter, periodic properties of elements, the interaction between the particles in liquid and solid phase, solutions, chemical equilibrium, acids and bases, solubility, and relation between chemical reactions and work.

(One term course for students of EE, CE, IE, FDE, ENVE, CENG, AEE, ME.) Introduction to atomic and electronic structure, chemical bonding, molecular structure and bonding theories, properties of liquids, solids and solutions, chemical equilibrium, kinetics, thermodynamics, metal complexes, organic compounds and nuclear chemistry.

**Textbook****:**

Instructor's lecture notes.

**Calculus I**

Limit, continuity. Derivatives: differentiation rules, Newton’s method for finding roots, curve sketching, maxima and minima, mean value theorem, Taylor’s formula, L’Hospital rule. Integration: indefinite and definite integrals, finding areas, volumes and arc lengths, surface areas of revolution. Transcendental functions, hyperbolic functions. Integration techniques, improper integrals.

**Textbook****:**

Calculus and Analytic Geometry, Sherman K. Steın, A. Barcellos, McGraw Hill Inc.

**Computer Engineering Orientation**

An orientation course to provide counsel to the students about the Department and Computer Engineering in general. An introduction to ethical and legal issues related to computer programs. An introduction to the faculty and their activities. Visit to several Computer Centers in and outside the University.

The students will learn the Curriculum, the Faculty, and the research activities of the department. They will also learn the facilities such as network and the internet. In addition the students will learn Ethical and Legal Issues Related to Programming.

**Textbook****:**

Instructor's lecture notes.

**Introduction To Computer Eng. Concepts**

The course objective is to provide a basic understanding of fundamental concepts in computer science and engineering. To improve the skills to work with abstract notions for solving computational problems. Teaching a particular programming language is not a primary objective; the language will serve as a medium for experimentation.

Introduction to fundamentals of computer systems, including computer organization, operating systems, language processors and user interfaces. Introduction to algorithms and programming. Reasoning informally about the correctness and efficiency of programs. A functional programming language will be used for practical work.

By the end of the course, students will have gained the skills mentioned in course objectives.

**Textbook****:**

Instructor's lecture notes.

**Technical English I**

The course reinforces academic reading skills (finding the main idea, skimming, scanning, inferring information, guessing vocabulary from context, etc.) through reading selections on a variety of topics. It also aims at developing critical thinking, which enables students to respond to the ideas in a well organized written format. Other reading related writing skills such as paraphrasing and summarizing are also dealt with.

**Textbook****:**

Instructor’s lecture notes.

**2. SEMESTER**

**Physics II**

The goal of this course is to provide a calculus-based physics course to help students pursuing advanced studies in engineering develop conceptual understanding of physical principles, the ability to reason, and gain skills for problem solving.

Understand how phycists approach and solve problems in mechanics, apply those methods to solve problems of mechanics, use inductive reasoning and calculus level mathematics to solve problems in mechanics, engage in independent and collaborative learning, identify, find, and use the tools of information science as it relates to mechanics, critically evaluate both source and content of scientific information.

**Textbook****:**

Physics for Engineers and Scientist, Fishbane, Gasiorowıcz, Prentice Hall.

Bueche, F.L. D. Jerde, Fizik İlkeleri, Palme Yayıncılık, (Çev: Kemal Çolakoğlu).

**Calculus II**

Sequence and series of numbers, power series, Taylor series. Quadratic curves, polar coordinates. Analytic geometry in R, vector functions. Functions of several variables, partial derivatives, minimum and maximum problems. Multiple integrals and applications. Line integral, Green’s theorem. Surface integral, divergence theorem, Stokes’ theorem.

**Textbook****:**

Calculus and Analytic Geometry, Sherman K. Steın, A. Barcellos, McGraw Hill Inc.

**Basic Linear Algebra**

Matrices, determinants and systems of linear equations. Vector spaces, the Euclidian space, inner product spaces, linear transformations. Eigenvalues, diagonalization.

**Textbook****:**

Instructor’s lecture notes.

**Introduction to C Programming**

Problem solving. Input-operation-output process. Analysis and design of algorithms. Definiteness, finiteness, effectiveness of algorithms. Algorithm Language. Contants, variables and expressions. Arithmetical, relational and logical operators. Input-Output statements. Conditional and iterative statements. Vector and matrix representations. String manipulations. Subroutines and Functions. Applications on a structural programming language.

**Textbook****:**

Instructor’s lecture notes.

**Technical English II**

The overall aim is to develop students all four skills (writing, reading, speaking and listening) in Academic English.

The course reinforces academic writing skills. In this course students write different types of essays based on the ideas they are exposed to in the reading selections. The emphasis is on the writing process in which students go through many stages from brainstorming and outlining to producing a complete documented piece of writing.

**Textbook****:**

Instructor’s lecture notes.

**3. SEMESTER**

**Introduction to Differential Equations**

First and second order differential equations, existence and uniqueness of solutions. Linear homogeneous differential equations. Linear nonhomogeneous equations: the method of undetermined coefficients and variation of parameters, reduction of order. Series solution of second order equations. Syfirsstems of first order differential equations. Solution by Laplace Transforms. Numerical solution techniques.

**Textbook****:**

Instructor's lecture notes.

**Data Structures**

Recursion. stack, queue, lists and trees. Binary trees, binary search trees, operations on binary trees: constructing, tree traversals, inserting and deleting an element. Algorithm analysis: time and space complexity. sorting and searching. graphs.

**Textbook:**

Instructor’s lecture notes.

**Logic Design**** **

Number Systems: Binary, Octal and Hexadecimal Numbers, Number Base Conversions. Boolean Algebra and Logic Gates. Simplification of Boolean Functions: Map Method, Tabulation method. Combinational Circuits, Binary Arithmetic Elements, Decoders, Encoders, Multiplexers / Data Selectors, Demultiplexers / Data Distributors, Comparators, Programmable Logic Devices. Synchronous Sequential Logic: Latches, Flip-Flops, Triggering of Flip-Flops, Analysis of Clocked Sequential Circuits, Design Procedure, Design of Counters.

**Textbook:**

Instructor’s lecture notes.

**Electrical Circuits**

Circuit laws and basic elements. Resistive circuits, analysis methods. Network theorems. First and second order circuits. Sinusoidal steady-state analysis and power. Basic diode and transistor circuits.(Offered to non-EE students only).

**Textbook****:**

Instructor's lecture notes.

**Discrete Mathematical Structures**Fundamentals of logic, set theory, relations, functions, induction, graph theory, trees, introduction to algebraic structures and lattices.

**Textbook****:**

Instructor’s lecture notes.

**Technical English III
**

choosing appropriate presentation topics reading extensively to gather relevant data sorting through information expanding vocab and actively using topical words preparing and using visual aids adjusting language to spoken discourse using body lang effectively expressing and supporting opinions asking and answering questions listening actively and responsively learning debating procedure learning discussion management techniques carrying out field research and team work

The course aims at developing oral presentation skills. To this end, students are engaged in classroom discussions following advanced reading texts on a variety of topics. In the course students study effective presentation techniques, do extensive reading and carry out research to give presentations of different functions with mature content and topical vocabulary.

**Textbook****:**

Instructor's lecture notes.

**Applied**** Seminar**

Presentation of the project work of the courses during the period.

**Textbook:**

Instructor’s lecture notes.

**4. SEMESTER**

**Object Oriented Programming**

Object-Oriented Programming Concepts. Exception handling. I/O Streams and Decorator Pattern. Concurrency. GUI Development. Security Issues. Objects over Networks. Database Connectivity. Serialization and Deserialization. Remote Method Calls. Introduction to Enterprise Components.

**Textbook****:**

Instructor's lecture notes.

**Automata**

Computer Science needs mathematical languages to abstract away from particulars of computing machinery and to concentrate on systematicity, capacity, and efficiency of computing in the abstract. Theory of formal languages studies such languages while automata theory studies their acceptors. Both theories have found scientific and practical use in all areas of computer science and engineering. In fact, description of any computational process can be recast in formal language theory or automata theory. From this perspective, the theory can be seen as a vehicle for communicating the ideas clearly and precisely among computer scientists. This course is an introduction to these topic.

Introduction to strings, languages and grammars. Concept of abstract machines and language acceptance. Deterministic and non-deterministic finite state machines. Regular expressions. Machines with pushdown tape. Turing Machines and recursive functions.

**Textbook****:**

Instructor's lecture notes.

**Numerical Methods**

Surveys and applications of numerical techniques related to matrix inversion, systems of linear equations and optimization, finite difference expressions, interpolation and approximation, numerical differentiation and integration. The problem of speed, accuracy and applicability of the topics are examined with related algorithms.

**Textbook:**

Instructor’s lecture notes.

**Digital Electronic Circuits**

This course aims to teach to non-EE students the fundamentals of digital electronics and logic, logic families and interfacing concepts.

Semiconductor diodes. Diode characteristics. Diode circuits. Transistors, BJT, FET and integrated circuits. Inverters. TTL, MOS, ECL structures. Logic Gates. Flip-flops. Bistable, astable and monostable multivibrators. Semiconductor memories. ROM, RAM structures. Programmable logic arrays.(Offered to non-EE students only)

**Textbook****:**

Instructor's lecture notes.

**Probability and Statistics**

Set theory, axioms and basic theorems of probability, sample space, experiments and events, rules for counting sample points. Permutation and combination. Axioms of probability, conditional probability. Bayes's theorem. Random variables, expected value, variance, moments, Chebyshev inequality. Some discrete and continious distributions. Organisation and analysis of data, frequency tables, graphical representations, measures of central tendency, measures of dispersion, sampling distribution and estimation, point estimation and interval estimation, confidence intervals, hypothesis testing, tests of significance based on Chi-square, regression and correlation.

**Textbook:**

Instructor’s lecture notes.

**Computer Organization**

Assembly language and machine language. Addressing modes, computer arithmetic, data path and control, micro programming, pipelining, memory hierarchy, I/O systems.

**Textbook:**

Instructor’s lecture notes.

**Internship-I**

Students will do their summer practice in a computerized industrial enterprises.

**5. SEMESTER**

**Analysis of Algorithms I**

This course aims to study the methods for designing efficient algorithms and to evaluate their performance (mainly in terms of time).

**Textbook****:**

Instructor's lecture notes.

**Database and Database Programming**

Database processing: file processing systems vs. database processing systems; components of database applications. Foundations of relational ımplementation: ıntegrity constraints; relational data manipulation; relational algebra. Data modeling: entity/relationship modeling, semantic object modeling. normalization: normalization process; normal forms; denormalization. Databases and Internet technology: three-tier architecture; the role of web and database servers, ODBC, OLE DB and ADO. Managing multi-user databases: concurrency control, database recovery, database security, database administration.

**Textbook:**

Instructor’s lecture notes.

**Microprocessor Systems**

Arithmetic operations with hexadecimal number system. Introduction to microprocessor architecture. Programming concepts. Instruction sets. Assembly language programming.

**Textbook:**

Instructor’s lecture notes.

**File Structures and Organizations**

The objectives of the course are to enable the students to understand the fundamentals of file organization and processing techniques and also the internals of the storage and retrieval components of relational database systems.

Sequential files. Unordered sequential files. Ordered sequential files. External sorting. Heap sort. Replacement selection sort. Large memory sorting. B+tree index. Hashing. Classical hashing. Linear hashing. Introduction to DBMSs. Relational databases. Relational query languages. Relational algebra. Relational calculus. SQL. QBE. QUEL. Implementing the join operation. Entity-Relationship data model.

At the end of the course, the student is expected to be able to design and implement fundamental file structures and to build and query a relational database using SQL.

**Textbook****:**

Instructor's lecture notes.

**Turkish I**

This course, according to regulations of the Council of Higher Education (YÖK), is a must course to all foreign students in Turkey. It is designed to equip them with a general knowledge on the process of the establishment of the Turkish nation-state in 1923. It covers economic, social, political and cultural developments in Turkey between 1908 and 1938.

**Textbook****:**

Instructor’s lecture notes.

**Applied**** Seminar**

Presentation of the project work of the courses during the period.

**Textbook:**

Instructor’s lecture notes.

**6. SEMESTER**

**Data Communications and Networking**

Introduction for computer networks performance evaluation. Modeling of traffic flows. Delay and loss models for computer networks. Networks of queues. Performance evaluation of multiple access methods and local area networks. Measurement and simulation of computer networks.

**Textbook****:**

Instructor's lecture notes.

**Analysis of Algorithms II**

This course aims to study the methods for designing efficient algorithms and to evaluate their performance (mainly in terms of time).

**Textbook****:**

Instructor's lecture notes.

**Operating Systems**

Operating Systems, History of operating systems, Process Concept: States & process control blocks, OS Kernel, Concurrent Processes, Mutual exclusion, Process Synchronization, Semaphores, Memory Management & Scheduling, Fixed & Multiple Portitioned multiprogramming, Virtual Memory, Paging & Segmentation, On demand paging & segmentation, Operations on Moving Head Disks, Disk Scheduling Policies, File & Database Systems, File System Functions, Blocking and Buffering, File Organization, Back-up & optimization, Database Systems & Models.

**Textbook:**

Instructor’s lecture notes.

**Software Engineering**

Introducing a breadth of concepts and techniques for the development of software within an engineering project perspective. Creating awareness for the quality and severity of software aspects.

Software project management: Metrics, estimation, planning. Software requirements analysis techniques. Software design techniques. Software implementation. Software quality assurance. Software testing. Software maintenance. Review of CASE technology.

**Textbook****:**

Instructor's lecture notes.

**Internship-II**

Students will do their summer practice in a computerized industrial enterprises.

**7. SEMESTER**

**History I**

This course, according to regulations of the Council of Higher Education (YÖK), is a must course to all foreign students in Turkey. It is designed to equip them with a general knowledge on the process of the establishment of the Turkish nation-state in 1923. It covers economic, social, political and cultural developments in Turkey between 1908 and 1938.

**Textbook****:**

Instructor’s lecture notes.

**System Programming**

Hypothetical machine architectures. Mainframe computer architecture: Overview of IBM System 370 architecture and assembly language. Assembler design. Loaders and linkers. Concurrent programming and examples. System level programming: using Win32 API.

**Textbook:**

Instructor’s lecture notes.

**Business Law**

Preamble: law concept in general: definition of law, sources, sections and functions; basic concepts of labor law: employee concept, employer concept, workplace concept. application of labor law: establishment authorized to apply the labor law, application area of the labor law. Individual labor law: employment agreement concept: definition of the employment agreement, elements of the employment agreement.

**Textbook:**

Instructor’s lecture notes.

**Artificial Intelligence Systems**

Representation of knowledge. Search and heuristic programming. Logic and logic programming. Application areas of artificial intelligence: Problem solving, games and puzzles, expert systems, planning, learning, vision, and natural language understanding. Exercises in an artificial intelligence language.

**Textbook:**

Instructor’s lecture notes.

**Graduation Project I**

Thesis that are related with using different technologies in various environments and application domains are given.

**8. SEMESTER**

**Computer Ethics**

Course Content: Philosophical ethics. Professional ethics and rules of conduct. Data protection, privacy. Software ownership, copyright, patent, license agreements. Internet problems, domain names, freedom of expression. Social implications of information technology, digital divide, work life.

**Textbook****:**

Instructor's lecture notes.

**Economics**

Basic Ideas and Concepts in Economics, Economic Systems, Price Theory, Consumption Theory, Production Theory, Costs and Production, Equilibrium in Perfect Competition Market, Equilibrium in Monopoly Market, Monopolistic Competition and Oligopoly, Factor Markets, Measuring the National Income, Economic Growth, Employment and Inflation.

**Textbook****:**

Instructor's lecture notes.

**Graduation Project II**

Thesis that are related with using different technologies in various environments and application domains are given.

**ELECTIVE COURSES**

**Data Mining**

Relationship between databases, data warehouses, data mining, and machine learning. Data preprocessing and cleansing. Knowledge Representation. Association Mining. Segmentation. Feature Extraction, Classification and Regression. Case studies: Basket Analysis and Credit Risk Scoring.

**Textbook****:**

Instructor's lecture notes.

Introduction to information storage and retrieval (IR). User perspective, search models, evaluation of IR systems. Formal IR models. Data structures and techniques including, inverted files, signature files, information filtering, clustering and cluster-based retrieval, hypertext and multimedia systems. IR and the Internet, browsing strategies, search engines, web robots and intelligent agents.

**Textbook****:**

Instructor's lecture notes.

**Computer Graphics**

Introduction. Basic computer graphics principles. Graphics processors. Graphic cards. Graphics primitives. Polygons. Transformations. Two and three dimensional computer graphics.

**Textbook****:**

Instructor's lecture notes.

**E-commerce**

Introduction to Electronic commerce (EC), EC tools; general concepts, tools and elements of EC, advantages of EC, Security and Legal Issues in EC, Marketting and retailing in EC, Technologies supporting EC, Electronic Data Interchange, Electronic payment systems.

**Textbook****:**

Instructor's lecture notes.

**Control Systems-I**

This courses aims to study of the basics of control system,describe a general process for designing a control system,understand the purpose of control engineering,examine examples of control systems,understand the principles of modern control engineering.

**Textbook****:**

Instructor's lecture notes.

**Control Systems-II**

Control of systems with more than one input and one output signals (MIMO-Systems)

**Textbook****:**

Instructor's lecture notes.

**Distance Learning Management**

This lesson’s contents are the definition of distance learning, history of distance education, Open and Flexible Learning Environments, Online (Online) Learning Models and Applications, Asynchronous and Synchronous Communication Environments and Tools, the types of distance learning, free and commercial distance learning systems, the features of distance learning systems, Basic Concepts of Management of Distance Education and Distance Education, Distance Learning Management Functions, etc.

**Textbook****:**

Instructor's lecture notes.

**Parallel Computing**

The aim of this course is to provide students with knowledge and hands-on experience in developing applications software for processors with massively parallel computing resources. In general, we refer to a processor as massively parallel if it has the ability to complete more than 64 arithmetic operations per clock cycle. Many commercial offerings from NVIDIA, AMD, and Intel already offer such levels of concurrency. Effectively programming these processors will require in-depth knowledge about parallel programming principles, as well as the parallelism models, communication models, and resource limitations of these processors. The target audiences of the course are students who want to develop exciting applications for these processors, as well as those who want to develop programming tools and future implementations for these processors.

**Textbook****:**

Instructor's lecture notes.

**Cryptology**

Basic concepts of cryptography and cryptanalysis. Classical methods: DES and other algorithms. Public key systems: RSA, El-Gamal and other algorithms. Number theory and complexity fundamentals. Digital signatures. Hash functions. Key distribution problems. Network aspects of cryptography. Secret sharing.

**Textbook****:**

Instructor's lecture notes.

**Robotics**

Designing, building and programming mobile robots; sensors, effectors, locomotion, basic control theory, control architectures, path planning, localization, mapping, learning. Teamwork; robot contest.

**Textbook****:**

Instructor's lecture notes.

**Image Processing**

Mathematical Representation of Images, Image Sampling and Quantization, Image Transforms: Fourier, Karhunen-Loeve, etc., Image Enhancement: Statistical Techniques and Ad-Hoc Techniques, Image Restoration: Inverse Filtering, Statistical and Algebraic.

**Textbook****:**

Instructor's lecture notes.

**Signal Processing**

Discrete time signals and systems, the z-transform, sampling of continuous time signals, transform analysis of linear time-invariant systems, Filter design techniques, the discrete Fourier transform, Computation of the discrete fourier transform, Fourier analysis of signals using the discrete Fourier transform.

**Textbook****:**

Instructor's lecture notes.

**Multiprocessor Systems**

Computer architecture, technology, and applications evolve together and have very strong interactions.Parallel computer architecture is no exception. A parallel computer is a collection of processing elements that cooperate and communicate to solve large problems fast.

The goal of this course is to articulate the principles of computer design at the multiprocessor level.

We will examine the design issues present for each of the system components – memory systems, processors, and networks – and the relationships between these components.

The contents of this course are “Intoduction to parallel architectures, Shared Memory Multiprocessors, Directory-based Cache Coherence, Hardware-Software Tradeoffs and Interconnection Network Design”

**Textbook****:**

Instructor's lecture notes.

**Natural Language Processing**

This course is designed to introduce students to the fundamental concepts and ideas in natural language processing (NLP), and to get them up to speed with current research in the area. It develops an in-depth understanding of both the algorithms available for the processing of linguistic information and the underlying computational properties of natural languages. Wordlevel, syntactic, and semantic processing from both a linguistic and an algorithmic perspective are considered. The focus is on modern quantitative techniques in NLP: using large corpora, statistical models for acquisition, disambiguation, and parsing. Also, it examines and constructs representative systems.

**Textbook****:**

Instructor's lecture notes.

**Web Mining**

Crawling and indexing, topic directories, supervised and unsupervised learning, hyperlink analysis, structured data extraction, information integration, opinion mining, Web usage mining.

**Textbook****:**

Instructor's lecture notes.

**Text Mining**

Information needs and knowledge management issues, enhancing user experience of information provision and seeking, the business case for text mining. The text mining pipeline: information retrieval, information extraction and data mining. Fundamentals of natural language processing: linguistic foundations, levels of linguistic analysis. Approaches to text mining: rule-based vs. machine learning based vs. hybrid; generic vs. domain specific; domain adaptation. Dealing with real text: text types, document formats and conversion, character encodings, markup, low-level processes (sentence splitting, tokenisation, part of speech tagging, chunking). Information extraction: term extraction, named entity recognition, relation extraction, fact and event extraction; partial analysis vs. full analysis. Data mining and visualisation of results from text mining. Evaluation of text mining systems: evaluation measures, role of evaluation challenges, usability evaluation, the U-Compare initiative. Resources for text mining: annotated corpora, computational lexica, ontologies, computational grammars; design, construction and use issues. Issues in large scale processing of text: distributed text mining, scalable text mining systems.

**Textbook****:**

Instructor's lecture notes.

**Information Security**

Principles and underlying concepts for security policy setting and for management of information security. Fundamental security principles: confidentiality, integrity, availability. Principles of information systems analysis for security: concept of analysis, basic features of information systems, semiotic model. Principles of policy for security. Principles of risk and contingency; risk analysis and risk management. Nature of responsibility and policy in the management of security. Role of cryptography in secure systems. Secure payment systems: SET, digital certificates, trusted third parties. Case studies.

**Textbook****:**

Instructor's lecture notes.

**Machine Learning**

Overview of artificial learning systems. Supervised and unsupervised learning. Statistical models. Decision trees. Clustering. Feature extraction. Artificial neural networks. Reinforcement learning. Applications to pattern recognition and data mining.

**Textbook****:**

Instructor's lecture notes.