Basic computer knowledge: History of computing, terms and definitions, system components and processes, computer architecture, data storage, computer networks and internet, software engineering, problem solving and algorithm design, data structures, application fields of computers.

Course Objectives:

1. Developing a common understanding about computer science,

2. Gaining ability to describe, analyze and solve the problems,

3. Teaching the algorithmic thinking and design in a higher level,

4. Gaining basic knowledge about computer architecture, programming languages, data storage, software engineering and computer networks,

5. Teaching the application fields of computers.

Recommended or Required Reading:

J. G. Brookshear. Computer Science An Overview. 7th Ed. 2002. Addison-Wesley. ,G. Michael Scneider, Judith L. Gersting. (1995). An Invitation to Computer Science. 1st Ed. West Publishing Company, USA. ,George Beekman, Micheal J. Quinn. (2005). Computer Confluence. 7th Ed. Prentice Hall.

Grading:

Midterm: 25%

Homework: 20%

Research Presentation: 20%

Final: 35%

Basic concepts of data, data structures and data types: arrays, strings, linear structures, sequential searching and sorting techniques, stacks, queues, pointers, linked lists. Various forms of m-way search and B-trees.

Course Objectives

To improve the students understanding and abilities related to data structures and algorithms. To improve the students ability to program.

Recommended or Required Reading

Data Structures and Abstractions with Java, F.M. Carrano and T.M. Henry, 4th ed., Pearson, 2016.
Goodrich, M. T., Tamassia, R., & Goldwasser, M. H. (2013). Data structures and algorithms in Python. John Wiley & Sons Ltd.

Learning Outcomes

1. To be able to use and design abstract data types.
2. To be able to implement array and linked data structures.
3. To be able to design and analyze recursive data structures and algorithms.
4.  To be able to implement and use algorithms and data structures for searching.

Grading:

• Midterm Exam %25
• Homework %25
• Final Exam  %40
• Quiz %10

Submissions: Project, Report

Fundamentals of computer programming: sequence, decision, repetion, syntax, compilation, debugging and maintenance, procedures, parameters, arrays, object, top-down structured design, layout and style. The emphasis is on an engineering “right-first-time” approach to solving large problems using computers. Basic concepts of algorithmics and algorithmic terminologies.

Course Objectives

To give the students the fundamentals of computer programming: sequence, decision, repetion, syntax, compilation, debugging and maintenance, procedures, parameters, arrays, object, top- down structured design, layout and style, To introduce basic concepts of algorithmics and algorithmic terminologies.

Learning Outcomes:

1. To be able to provide algorithmic solutions for basic problems.
2. To be able to use and explain main concepts of programming.
3. To be able to design and apply decision structures.
4. To be able to design and apply loop structures.
5. To be able to monitor and analyze computer software.
6. To be able to design, define and use functions.
7. To be able to use recursive approach for problem solving.
8. To be able to design, define and use class.

References:

“Starting Out with Python,” Tony Gaddis
“The Practice of Computing Using Python,” Punch & Enbody, Global Ed.

“Python How to Program,” Deitel & Deitel

“Think Python: How to Think Like a Computer Scientist” by Allen B

Submissions:

Homework-1 Grading: Yes
Homework-2 Grading: Yes
Homework-3 Grading: Yes
Homework-4 Grading: Yes

Grading:

Midterm exams: 20% * 2

Final exam: 30%

Assignments: 5% * 4

Quizzes: 1% * 10

Prerequisites: There are no prerequisites for the course.

Supplementary Courses: Since this is an introductory course to programming, no supplementary courses are required.

Logic, propositions and proof methods. Sets. Functions, sequences and summations. Algorithms and complexity analysis. Integers and division. Matrices. Mathematical induction. Recursive definitions and algorithms. Probability theory. Counting techniques. Graphs. Trees.

Course Objectives

1.To teach the problem solving strategies, techniques, and tools
2.To provide students with the ability of logical and mathematical thinking
3.To form the background related to the discrete mathematics concepts and the corresponding mathematical notations
4.To develop the critical thinking ability by introducing algorithmic solutions to some abstract problems

Recommended or Required Reading

Zeitz, Paul; 1999; “The Art and Craft of Problem Solving”; John Wiley & Sons, Inc. ,Rosen, Kenneth H.; 2007; “Discrete Mathematics and its Applications”; 6th Ed.; McGraw-Hill ,Stein, Clifford, Drysdale, Robert L., Bogart, Kenneth; 2011; “Discrete Mathematics for Computer Scientists”; Pearson

Learning Outcomes:

1. To be able to compare and use different discrete structures
2. To be able to analyze problems and identify appropriate solution schemes
3. To be able to demonstrate abstraction ability

Grading

Midterm 25%

Quiz 15%

Homework 30%

Final 30%

Control Structures, Classes and Methods, Inheritance, Polymorphism, Interfaces, Exception Handling, Streams and File I/O, Recursion, Dynamic Data Structures and Generics.

Course Objectives

To provide students with the ability of object oriented programming

To teach object oriented software design at beginners level

To teach object oriented coding of fundamental algoritms and data structures

To teach Java API definition and use at intermediate level

To provide students with the ability of simulation programming.

Recommended or Required Reading

Walter Savitch; 2012; “Java: An Introduction to Problem Solving and Programming”; 6th Ed.; Upper Saddle River, NJ: Prentice-Hall; ISBN 0-273-75142-5 ,Deitel&Deitel; “Java How to Program”; 9th Ed.; Upper Saddle River, NJ: Prentice-Hall; ISBN 0-273-75976-0

Learning Outcomes:

Upon the completion of this course a student :

1. Apply the operations for maintaining common data structures in object-oriented way
2. Design algorithms in object-oriented way for solving computing problems
3. Know and compare different programming approaches
4. Analyze problems, design and implement solutions
5. Demonstrate the ability to abstract in object oriented context
6. Apply computer engineering methods to real world problems

Grading

Midterm 25%

Quiz 20%

Homework 20%

Final 35%

In this course, the fundamental concepts of programming languages and different programming paradigms will be covered. Students will gain knowledge in the following areas: Introduction to Programming Languages: The history of programming languages, types, and fundamental features. Programming Language Syntax: The structure of programming languages, syntax rules, and structural elements of a language. Names, Scopes, and Bindings: Relationships between variables, functions, and other constructs, scope concepts, and how bindings work. Control Flow: Conditional statements, loops, and control structures for managing program flow. Functional Languages (Introduction to Scheme): Functional programming paradigm, features of functional languages, and an introduction to the Scheme language. Data Types: Definition of data types, basic data structures, and type conversions. Subroutines and Control Abstraction: Use of subroutines, functions, and procedures, and control abstraction techniques. Logic Languages (Introduction to Prolog): Logic programming paradigm, fundamentals of Prolog, and practical applications. Data Abstraction and Object Orientation: Object-oriented programming, classes, objects, inheritance, and abstraction. Throughout the course, students will be exposed to how various programming languages are used in practice, as well as a comparison of languages from different paradigms. Additionally, essential programming skills, such as understanding the compilation and execution phases of a program and writing high-level functions, will be developed.

Course Objectives

The aim of this course is to teach students the fundamentals of programming languages from different paradigms. Students will be able to understand the concepts of syntax, scope, and binding in the context of programming languages and write programs based on these concepts. Additionally, they will gain knowledge of functional and logic programming languages and learn the basic concepts related to these paradigms through practical applications. By the end of the course, students will have the skills to easily learn a new programming language and write high-level functions.

Recommended or Required Reading

• M. L. Scott, Programming Language Pragmatics (4th Edition), Morgan Kaufmann, 2016.
• R. W. Sebesta, Concepts of programming languages (11th Edition), Pearson, 2016.

Learning Outcomes
1. Being able to use languages belonging to different programming paradigms.
2. Demonstrating the ability to easily use a new programming language.
3. Being able to explain the compilation and execution phases of a program.
4. Being able to write high-level functions.

Assessments

Midterm 40%

Homework 20%

Final 40%

Abstract automata, especially finite state machines; push-down automata; and Turing machines. Formal languages, especially context-free languages. The relationship between automata and languages. Computability and solvability.

Course Objectives

To teach mathematical proof making.

To teach the concepts of automata, formal languages, and Turing machines.

By giving the theoretical grounds in the construction of computers, to ensure the apprehension of the limitations of computing machines.

Recommended or Required Reading

Lewis, H. R., & Papadimitriou, C. H. (1998). Elements of the Theory of Computation. ACM SIGACT News, 29(3), 62-78.
Sipser, M. (1996). Introduction to the Theory of Computation. ACM Sigact News, 27(1), 27-29.

Learning Outcomes

1. To be able to classify language definitions.
2. To be able to analyze problems and provide appropriate representations for them.
3. To be able to show abstraction ability.
4. To be able to identify some difficult problems in computer science. 

Grading

Midterm 35%

Final 45%

Homework 20%

The course content provides the student with the basic knowledge, concepts and experiences required to understand and design computer architecture. The content includes theoretical and experimental studies such as boolean algebra, logic gates, multi-level logic gates, combinational arithmetic and logic systems, sequential logic, registers and counters, memory units and problem solving with programmable logic. The student’s ability to design logic and arithmetic circuits theoretically, test them and apply them in a laboratory environment is developed.

Course Objectives

Introduction to computer architecture. Number systems. Boolean algebra. Logic gates and flip-flops. Combinational and sequential circuit design. Strings, counters. Bus transfer. RAM, ROM units. Instruction execution and loop control.

Learning Outcomes:

1. To be able to understand computer architecture and processes.
2. To be able to understand how arithmetic and logic processing unit works and the ability to design.
3. To be able to understand memory unit and the ability to understand how it works.
4. To be able to design circuitry for defined problems.

Recommended or Required Reading

• Final Exam: 20%
• Midterm Exam: 30%
• Lab: 40%
• Homework: 10%

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 problems of speed, accuracy and applicability of the topics are examined with related algorithms. The applications of these numerical methods and subjects on computers using efficient programming techniques and with necessary programming languages.

Learning Outcomes:

1. To be able to explain effects of  finite representation of real numbers for a given algorithm.
2. To be able to find numerical errors in calculations and compare numerical errors of different algorithms for the same problem.
3. To be able to solve numerical problems that include derivative, integral, interpolation and/or optimization.
4. To be able to apply recursive solutions to numerical problems.
5. To be able to implement linear/non-linear systems for the given problem.
6. To be able to apply appropriate numerical algorithm for given linear/non-linear system.

Reference book(s):

The role of algorithms in computing, Growth of functions, recurrences, probabilistic analysis and randomized algorithms, dynamic programming, greedy algorithms, advanced data structures, graph algorithms, NP-Completeness.

Course Objectives

With both the theoretical and practical knowledge gained through this course a student is expected to have the necessary qualifications and background to be able to solve the mathematical problems encountered in real life situations

Recommended or Required Reading

Introduction to Algorithms, 3rd Ed., T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, MIT Press

Learning Outcomes:

1. To be able to propose a solution using algorithmic approach
2. To be able to identify algorithm design alternatives for a given problem
3. To be able to analyze the complexity of applications

Grading

Midterm 35%

Homework 20%

Final 40%

Quiz 5%

This course provides an elementary introduction to probability and statistics with an emphasis on experimentation using computer programming. Topics: permutation, combination, random variables, probability distributions, Bayesian inference, hypothesis testing and confidence intervals.

Course Objectives

Elementary probability theory, conditional probability and independence, random variables, distribution functions, joint and conditional distributions, law of large numbers, central limit theorem, parameter estimation, confidence intervals, and hypothesis testing.

Recommended or Required Reading

Peter Olofsson, Mikael Andersson, Probability, Statistics, and Stochastic Processes, Wiley. ,Michael Baron, Probability and Statistics for Computer Scientists, CRC Press. ,Allen B. Downey, Think Stats: Probability and Statistics for Programmers, Green Tea Press.

Learning Outcomes:

1.To be able to explain and apply the concepts of probability and random variables.
2.To be able to describe and use common probability distributions and their properties.
3.To be able to employ descriptive statistics.
4.To be able to estimate distribution parameters.
5.To be able to calculate confidence intervals.
6.To be able to conduct hypothesis testing.
7.To be able to utilize computation as a tool to explore concepts in probability and statistics and to do quantitative analysis.

Grading

Final Exam 40%

Quiz 10%

Homework 20%

Midterm 30%

Basic computer organization concepts. Instruction set architecture. Assembly-level programming. Processor design. Pipelining. Data and control hazards. Memory hierarchy and cache. Parallelism and system performance.

LECTURES & LABS

Lectures: 3 hours each week

Lab sessions: 2 hours each week

OBJECTIVE

To equip students with fundamental knowledge of computer organization and design.

1. Understand basic computer organization concepts.
2. Design and implement assembly-level programs.
3. Understand processor design and instruction execution.
4. Design and implement a single-cycle processor using HDL.
5. Understand memory hierarchy and cache.
6. Evaluate system performance.

LEARNING OUTCOMES

1. Ability to design general-purpose and application-specific microprocessors
2. Ability to demonstrate experience in microprocessor implementation and testing
3. Ability to program in assembly language

TEXTBOOK

Computer Organization and Design: The Hardware/Software Interface (The Morgan Kaufmann Series in Computer Architecture and Design), 5th Edition, by David A. Patterson, John L. Hennessy.

Overview of computer networks; packet switching, Internet, layered architecture; network applications; web, email, domain name system; reliable data transfer, congestion and flow control, UDP, TCP; switching, forwarding, routing, IP; medium access control, local area networks, Ethernet.

LECTURES

3 hours each week

OBJECTIVE 

To equip students with fundamental knowledge of computer networks.

1. Understand the general structure of computer networks and the layered architecture.
2. Examine the operating principles of fundamental network protocols.
3. Analyze the algorithms used for routing, congestion control, flow control, medium access control.

LEARNING OUTCOMES

1. Ability to analyze communication systems using fundamental concepts of communication.
2. Ability to analyze the layered network model and Internet protocols.
3. Ability to design networks and protocols.

TEXTBOOK

Computer Networking: A Top-Down Approach 8th edition, Jim Kurose, Keith Ross, Pearson, 2022.

Introduction to Database Management Systems, Architecture of Database Management Systems, Relational Model, Relational Database Design, Relational Algebra, SQL (DML), SQL (DDL), Normalization, Consistency and Security, Query Optimization, Concurrency, Term Projects

Course Objectives

Gain ability to design, implement and query a database which is a component of an information system, teach the methods and techniques to assure the consistency in databases to make them to be able to create and enhance available databases, make it possible to understand and use query techniques in order to be efficient users of data management layer of an information system.

Recommended or Required Reading

Edward Sciore, Database Design and Implementation, Second Edition, Wiley, 2020. ,Raghu Ramakrishnan and Johannes Gehrke, Database Management Systems, McGraw-Hill, 2001, 3rd Edition. ,J. Ullman, Principles of Database Systems, Second Edition, Computer Science Press, 1982. ,R. Elmasri, S.K. Navathe, Fundamentals of Database Systems, Third Edition, Addison-Wesley, 2000.

Learning Outcomes:

Upon the completion of this course a student :

1. Capacity to design a relational database

2. Capacity to create a relational database

3. Master relational algebra and SQL

4. Learn concurrecy methods, normalization, security and consistency methods in database management systems

5. Use databases

Grading

Midterm 25%

Application / Practice 10%

Research Presentation 25%

Final 40%

Reference Standards

– Software Design: IEEE STD 1016 – Systems Design—Software Design Descriptions
– Software Test: IEEE STD 29119 – Software and Systems Engineering — Software Testing

Course Objectives

By the end of this course, students will gain knowledge and practical skills in:
– The role of the software engineer, Software engineering principles, Software development life cycle (SDLC) models, Requirements analysis, specification, system design, Verification and validation processes, Use of software quality metrics, Team organization in software projects, and Human-computer interaction (HCI) design.

Weekly Course Schedule

Deliverables and Evaluation

Each student group (5–8 members) will complete the following artifacts based on a given project topic:
– Requirements Analysis
– Software Design Document (SDD)
– Test Report
– Software Implementation (working system)

Submission and Evaluation Table

Grading Breakdown

Learning Outcomes

C1. Identify functional requirements for a given project

C2. Distinguish between functional and non-functional requirements

C3. Apply object-oriented design methodology

C4. Prepare a project definition document

C5. Utilize and apply software development process models

Prerequisite Courses

– Fundamentals of Programming (CENG211)
– Information Management (CENG315)
– Project Management (CENG323)

Having prior knowledge and experience in analysis and design methodologies will be very beneficial.

The course provides the main operating system functionalities and implementation details. It covers basic operating systems concepts, introduces process concepts and process management policies, discusses main memory and virtual memory techniques, and storage and I/O management issues.

Course Objectives

1. Understand basic operating systems concepts.

2. Understand process/thread execution, synchronization, and scheduling issues.

3. Understand operating system memory management policies.

4. Understand storage management techniques.

Recommended or Required Reading

A. Silberschatz, P.B. Galvin, G. Gagne: Operating System Concepts, 10th Edition, John Wiley & Sons, 2018.

Learning Outcomes:

1. Analyze and design modern architecture and operating systems by acquiring the basic concepts of operating systems.

2. Analyze systems that include operating systems and identify related problems.

3. Compare different solution methods for operating system problems and choose the most appropriate one.

Grading

Midterm 20%

Assignments 40%

Final 40%

Reference book(s):

• IEEE Std 830-1998, IEEE Recommended Practice for Software Requirement Specifications
• IEEE 16326- Systems and Software Engineering – Lifecycle Processes- Project Management
• IEEE Std 1058- Standard for Software Project Management Plans

Course Objectives:

By the end of this course, students will be able to:

Comprehend the software development lifecycle, covering aspects such as problem analysis, software requirements analysis, and software project management.

Submissions/Homeworks/Projects:

In this course, a project description is given to the class, and students form teams of 5-8 members.  Each team participates in a range of activities, from preparing the project charter to problem analysis, requirements analysis, and software project management. The output documents of these activities are the Project Charter, the Problem Analysis Report (including AS-IS and TO-BE), the Software Requirement Specification, and the Software Project Management Plan.

Grading:
Project Charter %2
Problem Analysis %15
– PA Review %3
SRS %15
– SRS Review %3
SPMP %15
– SPMP Review %3
Final Exam %40
Peer Assessment %14

Course Learning Outcomes:

C1.To be able to recognize the personal and technical properties required for a project manager.
C2.To be able to identify the fundamental steps of project management and risk factors.
C3.To be able to describe the outlines of preparation, planning, application, inspection and completion phases of a project.
C4.To be able to tell the fundamental economic concepts required for a project manager.
C5.To be able to demonstrate computer-aided project planning and management with examples using MS Project software.

Overlapping with or complementing topics in courses:

Familiarity with the fundamentals of the field

• Programming Fundamentals (CENG211) is a must.
• You might be taking Information Management (CENG315) at the same time.
• Prior knowledge and experience of an analysis and design methodology will be very beneficial.

A brief review of the concepts of probability, random variables, and parameters; probability distributions; moments; inequalities related to probabilities; the central limit theorem; stochastic processes; the Poisson process; birth-death processes; Markov chains; an introduction to queueing theory and its fundamental concepts; Little’s Law; and M/M/1 queueing systems.

Course Objectives

To develop the ability for stochastic, logical, and mathematical thinking. To build the foundational knowledge necessary for understanding stochastic systems. To equip students with the ability to analyze and interpret simple queueing systems. To foster the ability to understand and interpret certain performance criteria of stochastic systems. To possess knowledge of classical and computationally intensive statistical methods, and to be able to perform probabilistic modeling.

Recommended or Required Reading

Arnold O. Allen.(1990) Probability, Statistics, and Queueing Theory with Computer Science Applications. (2nd Ed.), Academic Press, Boston,Sheldon M. Ross (1996) Stochastic Processes, (2nd Ed.) J. Wiley & Sons. New York

Learning Outcomes

1.The ability to understand and define random variables.

2.Gaining knowledge of stochastic processes and the ability to perform basic analyses.

3.The ability to understand queueing systems and to analyze simple queueing models.

4.The ability to compute and analyze certain performance criteria in queueing systems.

Grading

Midterm: 40%

Final: 60%

An introduction to Shannon’s information theory and elementary binary coding schemes with and without noise. The concept of information, entropy, simple sources, Markov sources, continuous sources, information channels, average error, ambiguity, transformation, capacity, noiseless coding, Kraft-McMillan theorem, Shannon-Fano and Huffmann coding schemes, error-correcting codes, linear codes, cyclic codes. Data Compression.

Course Objectives:

To teach the fundamental concepts of information theory. To teach that how to define and formulate the problems of communication and signal processing. Teaching that how to solve the problems for communication and signal processing.

Recommended or Required Reading:

T. M. Cover, J. A. Thomas, ‘Elements of Information Theory’, 2nd. Edition, Wiley,2006 ,P. S. Nuggehalli, ‘Information Theory and Coding’, CEDT, IISc, Bangalore, http://nptel.iitm.ac.in/courses/Webcourse-contents/IISc-BANG/Information%20Theory%20and%20Coding/Learning%20Material%20-%20ITC.pdf ,R. G. Gallager, ‘Principles of Digital Communication’, Chapters 1 to 3, Cambridge Univ. Press, 2008 ,R. G. Gallager, ‘MIT 6.450 Principles of Digital Communications I, Lectures 1 to 7’; YouTube: MIT OCW, 2006 ,R. W. Yeung, ‘A First Course in Information Theory’, NY: Kluwer Academic/Plemu Publishers, 2002 ,A. E. Gamal and Y.-H. Kim, ‘Lecture Notes on Network Information Theory’, online, 2010

Learning Outcomes:

1. To do analyzing and modeling by using of fundamental concepts of information theory for signal processing and communication.

2. Analyzing, defining and doing research capability for the new requirements of information theory depend on technological evolution.

3. To have the ability for analyzing, developing and using of information theory for interdisciplinary projects.

4. To be able to measure and analyse the signal proceesing and communication quality and efficiency.

Grading:

Midterm: 25%
Homework: 20%
Research Presentation: 20%
Final: 35%

Specification and verification techniques for real-time systems with many interacting components. formal design of real-time systems using (a) programming languages with unambiguous semantics of time – related behavior and (b) scheduling algorithms. Real-time operating systems, concepts of programming languages for real-time systems. Synchronous programming languages for reactive systems and their mathematical background. Software development for real-time systems.

Course Objectives

To introduce the fundamentals of real-time systems. To be able to create real-time operating systems.

Recommended or Required Reading

Phillip Laplante, Real-Time Systems Design and Analysis – An Engineer’s Handbook, IEEE Press, 1993.

S.H.Son, Advances in real-time systems, Englewood Cliffs, N.J.: Prentice Hall, c1995.

Learning Outcomes

1. To classify various kinds of real-time systems
2. To identify real-time sytems and design by formal techniques.
3. To be able to create real-time kernels, and to develop programmes on operating systems
4. To demonstrate the ability to use formal methods

Grading

Written Midterm Exam: 20%

Written Final Exam:  30%

Assignments: 10%

8051 hardware and software. 8051 assembly language. 8051 architecture. 8051 I/O port programming, timer programming, serial port programming, interrupt control.

Course Objectives

Elements of microprocessors and microcomputers, software and hardware for microprocessors; microcontrollers; embedded system design with microcontrollers, memory interface, analog-digital input/output interfaces and interrupt interface of typical microprocessors/controllers; programming with assembly.

Recommended or Required Reading

The 8051 Microcontroller, 4th Edition (2007) Prentice- Hall [Scott MacKenzie, Raphael Chung-Wei Phan] (textbook)

Learning Outcomes

OC1: Teaching 8051 programming with assembly language.
OC2: Explain and compare the concepts of microprocessor and microcontroller.
OC3: To teach the 8051 microcontroller family and its architecture.
OC4: Explain the software and hardware features of 8051.

Grading

Midterm 40%

Final 20%

Lab 40%

Using propositional, predicate and temporal logics, in order to logically analyse, specify and solve complex problems.

Course Contents

1.Specify in mathematical logics sentences given in natural languages

2.Infer mathematical conclusions from specifications given in mathematical logics

3.Specify mathematically uncertainty, multi-valence interpretation and temporal change of complex problems

4.Solve sample complex problems using automated reasoning applications

Recommended or Required Reading

Mendelson, Elliott; 1997; “Introduction to mathematical logic”; Chapman& Hall; ISBN 0-412-80830-7 ,Bell,J.L.; 1997; “A course in mathematical logic”; 1997; N.H.; ISBN 0-7204-2844-0 ,Kelly, John; 1997; “The Essence of Logic”; ISBN 0-13-396375-6 ,Enderton, Herbert B.; 2001; “A mathematical introduction to logic”; Academic Press; ISBN 0-12-238452-0 ,Gabbay, Dov M.; 2000; “Temporal logic:mathematical foundations and computational aspects”; Oxford: Clarendon press; ISBN 0-19-853768-9

Learning Outcomes

1. Resolve ambiguities in natural language sentences

2. Specifies problems in mathematical logics

3. Explains mechanisms for automated reasoning

4. Develops logical systems

Grading

Midterm: 24%

Quiz: 6%

Homework: 10%

Presentation: 30%

Final: 30%

Analyse the mathematical basics of fuzzy logics; analyse properties of theoretical fuzzy logical systems; model flexible human logics using fuzzy systems; develop applications of sample fuzzy systems.

Course Objectives

1.Learn the mathematical basics of fuzzy logics.

2.Learn properties of fuzzy logical systems.

3.Develop sample applications that mimic the flexible logic of human.

Recommended or Required Reading

Nguyen, Hung. T; Walker, Elbert A.; 2005; “A First Course in Fuzzy Logic”, 3rd Edition; Chapman & Hall/CRC; ISBN-10: 1584885262; ISBN-13: 978-1584885269 ,Yen, John; Langari, Reza; 1999; “Fuzzy Logic: Intelligence, Control, and Information”; Prentice Hall; ISBN-10: 0135258170; ISBN-13: 9780135258170 ,Sivanandam, S.N.; Sumathi, S.; Deepa, S.N.; 2006; “Introduction to Fuzzy Logic Using MATLAB”; Springer; ISBN-10: 3540357807; ISBN-13: 978-3540357803

Learning Outcomes

1. Model adaptive logic using fuzzy logic

2. Simulate flexible system behaviours using fuzzy logic

3. Develop possibilistic systems

4. Evaluate reasonable system behaviour

Grading

Midterm: 10%

Quiz: 10%

Homework: 10%

Presentation: 20%

Final: 30%

Web and .NET framework, software development with ASP.NET, web services programming, MVC programming.

Recommended or Required Reading

Cal Henderson, Building Scalable Web Sites: Building, Scaling, and Optimizing the Next Generation of Web Applications, O Reilly Media, 2006. ,John Allspaw, The Art of Capacity Planning: Scaling Web Resources, O Reilly Media, 2008 ,Theo Schlossnagle, Scalable Internet Architectures, Sams, 2006

Learning Outcomes

Upon the completion of this course a student :

1. To introduce Web software development with ASP .NET
2. To describe Web software development methodologies on .NET platform
3. To provide students with the fundamental concepts of Web Services
4. To demonstrate software project development with MS Visual Studio

Grading

Midterm: 25%

Homework: 40%

Final: 35%

This course covers the fundamentals of programming mobile devices, how to access and use mobile device hardware, and the interaction between mobile apps and web services. Topics include the design and implementation of user interfaces on the Android platform, cross-platform mobile application development, and programming with sensor such as the camera and the GPS.

Course Objectives

To develop mobile device programming skills. To familiarize with the Android platform components. The develop practical skills in integrating mobile applications and web services.

Recommended or Required Reading

Android Programming: The Big Nerd Ranch Guide by Bill Phillips, Brian Hardy, 2nd Ed.

Learning Outcomes

Be able to explain the fundamental concepts of mobile device programming

Be able to design and implement mobile application user interfaces

Be able to design and implement mobile applications that utilize network services

Be able to analyze the user experience of mobile applications

This course provides a hands-on comprehensive study of Cloud concepts, the history and fundamentals of cloud computing across various cloud service models including IaaS, SaaS, PaaS, including requirements, constraints, architecture, principals, areas of implementation, advantages/disadvantages and comparison to hybrid and local architectures. PaaS topics covers a range of Cloud platforms such as Google App Engine, Amazon Web Services(AWS), Microsoft Azure and others. Topics include the design, development and implementation of cloud based mobile applications, web applications, databases and systems as well as migrating existing software applications to the cloud, working on hybrid solutions. Course offers wide range of case studies of globally recognized technology startups, products and brands which are built on cloud platforms and analyze and review them in terms of technology, operations, finance, branding and business plan aspects.

Course Objectives

To introduce cloud concepts, principals and evolution of cloud computing paradigm. To develop mobile application, web application and software development & programming skills on cloud architecture and cloud platforms. To discuss and understand parallel processing, storage, security, scalability, management issues and benefits of the cloud services and architecture.

Recommended or Required Reading

K. Hwang, G. Fox and J. Dongarra, Distributed and Cloud Computing: From Parallel Processing to the Internet of Things, Elsevier, 2012

Learning Outcomes

Be able to explain the fundamental concepts of cloud computing

Understand the advantages and disadvantages of cloud computing systems and be able to use tools on cloud platforms

When developing an application or system, be able to determine and decide on the appropriate platform and configuration by understanding non-functional requirements and considering aspects such as cost, performance, suitability, and security

Be able to design and implement mobile or web applications that run on PaaS platforms such as Amazon Web Services, Microsoft Azure, and Google App Engine

Be able to analyze and monitor applications built on cloud systems and optimize the system according to changing requirements

Be able to productize, brand, and commercialize software developed on cloud systems by establishing it as a company and launching it to the market

This course covers the basic image processing techniques and approaches for image content analysis.. The course content includes memory representation of images, basic image processing techniques, keypoint extraction and description, image matching and fundamentals of camera geometry.

Course Objectives

The main aim of the course is to familiarize the students with image processing and image content analysis. The course’s target audience is advanced undergraduate and beginning graduate students. The course will play a preparatory role for the graduate courses covering topics such as digital image processing and 3D photography.

Recommended or Required Reading

Computer Vision: Algorithms and Applications, R. Szeliski, 2010

Learning Outcomes:

Reference

Department of Computer Engineering Internship Directive

Course Objectives

The purpose of the internship is to enable students to put into practice the theoretical knowledge they have acquired in their field of study. Through this process, students begin to get acquainted with the professional world and start identifying the areas of interest that may shape their future careers. An internship also helps computer engineering students gain knowledge about the structure and management of an organization, as well as the information systems and system analysis conducted within the company. The internship allows students to engage in practical work, understand the overall structure of the firm, and participate in ongoing projects.

Weekly Plan

The internship must be completed in a minimum of 20 working days. Internship dates must not conflict with academic semester dates or the summer school period.

Deliverables

The following documents must be submitted at the end of the internship:

– Internship Report

– Company Survey

– Student Survey

Grading

Internship Report – 100%

Learning Outcomes

LO1. Ability to transfer theoretical knowledge acquired during university education to real-life applications.

LO2. Awareness of the computer engineering profession, including its roles and responsibilities.

LO3. Ability to observe the working environment during the internship and identify a preferred industry sector.

LO4. Ability to develop solutions individually or as part of a team for problems encountered in practice.

LO5. Ability to improve verbal and written communication skills and understand the importance of foreign language proficiency.

LO6. Ability to present acquired knowledge in the form of a formal report.

Prerequisite

It is recommended that students complete all coursework up to the end of the third year.

Study of the larger ethical and social issues of computing, including the role of a digital society in the modern world. Practical and theoretical applications of computer privacy and security, including network security measures and encryption protocols. Ethical theory and its application to problems in computing. Seminar discussion on valuesystems, social impact, and human factors, and about use and misuse of computers.

Course Objectives:

– To learn ethical concepts in information technologies

– To learn concepts about ethical theories

– Learning privacy and property rights issues

Learning Outcomes:

1.Understanding the ethical phenomenon and legal dimensions in information technology.
2.Understanding of general ethical concepts.
3.To be able to understand basics of privacy.
4.To be able to understand basics of property rights.

Recommended or Required Reading
“Computer Ethics”, Deborah G. Johnson, 4th Edition (2009) ,”Ethics for theInformation Age”, Michael J. Quinn, 8th edition (2020)

Grading:

Midterm 40%

Homework 10%

Final 50%

Writing technical reports, project management and planning, system analysis, requirement analysis, engineering ethics, data design, software design, presenting a project

Learning Outcomes:

1.To be able to identify problem.
2.To be able to analyze the problem, to define the scope, constraint, requirements, to be able to design a solution.
3.To be able to design a solution that is innovative, sustainable and beneficial to the environment and society.
4.To be able to learn and use new methods and technologies.
5.To be able to gain teamwork skills.
6.To be able to present written and oral presentation skills of the project.
7.To be able to describe the concepts about entrepreneurship and innovation.

Grading

Homework: 75%

Application/ Practical: 25%

Invited speakers, change management, coding documentation, coding standards, testing, reliability, security, protyping, user manuals, group demos.

Learning Outcomes:

1. To be able to identify problem.
2. To be able to analyze the problem, define the scope, constraint, requirements, and design a solution.
3. To be able to design a solution that is innovative, sustainable and beneficial to the environment and society.
4. To be able to learn and use new methods and technologies.
5. To be able to gain teamwork skills.
6. To be able to present written and oral presentation skills of the project.

Grading

Application/ Practical: 25%

Presentation: 75%

This course covers all aspects of TCP/IP and network programming. Starting with a review of IP and TCP, including its services and Ipv6, we will then learn about socket programming, web programming (PHP and MySQL), and time permitting, we will be exploring new trends in networking and programming.

Course Objectives:

To gain experience on TCP/IP programming. * To gain experience on PHP and MySQL web programming.

Recommended or Required Reading

Data Communications and Networking, Behrouz A. Forouzan ,Course Web Site: http://www.kalfaoglu.com/ceng421

Learning Outcomes

1. To acquire knowledge on TCP/P layers and its applications.

2. To program in C/C++ using TCP sockets

3. To be able to create a full-fledged web site using PHP, MySQL and HTML.

4. To improve the student s ability to write programs

Grading:

Midterm: 40%

Homework: 20%

Final: 40%

LAN/WAN analysis and design; LAN standards, internetworking, recent technologies, LAN design procedures; WAN design, network services, WAN design procedures; LAN management, SNMP (simple network management protocol); design tools, network simulators.

Course Objectives

To introduce analysis and design techniques for LAN and WAN. To give the latest network technologies.

Recommended or Required Reading

Arne Mikalsen, Per Borgesen, Local Area Network Management, Design & Security (Paperback), Wiley; 1st edition (June 15, 2002) ,Andrew S. Tanenbaum, Computer Networks, Prentice-Hall

Learning Outcomes

1. To have the necessary knowledge and experience to design LANs and WANs

2. To demonstrate the ability of analysing and configuring network protocols

3. To demonstrate the ability of designing a new protocol

4. To have the experience and familiarity with simulators and other tools to analyse and design networks

5. To know the fundamentals of network security

Grading

Midterm 30%

Homework 35%

Final 35%

Nature of embedded systems, particular problems, special issues; role in computer engineering; embedded microcontrollers, embedded software; real time systems, problems of timing and scheduling; testing and performance issues, reliability; low power computing, energy sources, leakage; design methodologies, software tool support for development of such systems; problems of maintenance and upgrade; networked embedded systems; FPGA design issues.

Course Objectives

To introduce embedded computer systems and applications together with their analysis and design.

Recommended or Required Reading

Berger, Arnold. Embedded systems design:an introduction to processes tools and tecniques. San Francisco;Lawrence, Kan: CMP Books, c2002 ,Adamski, Marian Andrzej. Design of Embedded Control Systems, Boston, MA : Springer Science+Business Media, Inc., 2005. ,Tebbs, David. Real time systems: management and design, McGraw-Hill London 1977 ,Herman Lam, John O Malley, Fundamentals of Computer Engineering: Logic Design and Microprocessors, Wiley. ,Enoch O. Hwang, Digital Logic and Microprocessor Design with VHDL.

1. To demonstrate the ability to model and design embedded systems.
2. To show ability to realize and verify systems.
3. To show ability to use embedded software development techniques.

Grading

Midterm : 10%
Homework : 10%
Final : 20%
Quiz : 10%
Lab : 10%
Project : 20%
Report : 20%

Object oriented analysis and design. UML usage. Advanced topics of object oriented programming. Software reuse, refactoring, unit testing. Fundamental design patterns. Software components, libraries and framework usage and ve creation.

Course Objectives

1. To provide students with the ability of object oriented analysis and design.

2. To provide students with the ability of object oriented design programming.

3. To teach reuse, refactoring, unit testing subjects.

4. To teach fundamental design patterns at intermediate level.

5. To provide students with the fundamentals of software components and software frameworks.

Recommended or Required Reading

X. Jia, Object-Oriented Software Development Using Java. , Addison Wesley. 2nd Edition, 2003. ,C. Larman, Applying UML and Patterns , Prentice Hall, 3rd Edition, 2004. ,M. Fowler, K. Beck, J. Brant, W. Opdyke, D. Roberts, Refactoring: Improving the Design of Existing Code , Addison-Wesley, 1999.

Learning Outcomes

Upon the completion of this course a student :

1. Know interleaved activities between specification, design, coding and testing

2. Use Object Oriented architecture components

3. Know and compare different software system development approaches

4. Analyze problems, design and implement solutions

5. Demonstrate the ability to abstract in software systems context

6. Apply software engineering methods to real world problems

Grading

Midterm 30%

Final 40%

Homework 30%

Course Objectives:
The objective of this course is to explain the Enterprise Applications, their usage areas and development methods. Students will be familiar with different types of Enterprise Applications, especially Enterprise Resource Planning (ERP) systems and its modules via SAP, which is one of the most common commercial ERP package programs. They will also learn basics of SAP ABAP, the popular enterprise programming language.

Submissions/Homeworks/Projects:

In this course, an ERP requirement document is given to project groups consisting of 5-6 members. Each team determines which SAP modules and processes will be included based on the requirement document and submits a detailed project report including FS (Functional Specification) and TS (Technical Specification) documents.

Grading:

Midterm %30
Final Exam %40
Project %30

Course Learning Outcomes:

C1. To be able to define and explain different types of Enterprise Applications

C2. To be able to describe the development and implementation strategies of Enterprise Applications

C3. To be able to demonstrate configuration, customization and programming of Enterprise Applications with examples using SAP sources

Logical / physical arhitecture design and server components, performance measurement, advanced caching strategies, horizontal scaling with MySQL and handling peak load.

Course Objectives

The goal of this course is to dive into advanced software development, architecture design topics for Internet projects. At the end of the term, expected outcome is to establish the basis of a background with which students can design and implement sites that can scale up to thousands of concurrent users.

Recommended or Required Reading

http://www.infoq.com/ ,http://aws.amazon.com/ ,http://www.slideshare.net/Blaine/scaling-twitter ,Cal Henderson, Building Scalable Web Sites: Building, Scaling, and Optimizing the Next Generation of Web Applications, O Reilly Media, 2006 ,John Allspaw, The Art of Capacity Planning: Scaling Web Resources, O Reilly Media, 2008 ,Theo Schlossnagle, Scalable Internet Architectures, Sams, 2006 ,Martin L. Abbott and Michael T. Fisher, The Art of Scalability: Scalable Web Architecture, Processes, and Organizations for the Modern Enterprise, Addison-Wesly, 2009 ,http://highscalability.com/ ,http://www.ahmetalpbalkan.com/blog/put-io-nun-altyapisi-ve-olceklenebilirligi-uzerine-roportaj/

Learning Outcomes

1. To be able implement sites that can scale up to thousands of concurrent users.

2. To develop a project on Play Framework.

3. To be able implement various cache mechanisms

4. To be able to perform horizontal scaling with MySQL and handling peak load

Grading

Midterm 15%

Research Presentation 60%

Final 25%

This course topics include types of enterprise application integration, their design and implementation issues as well as their qualites such as security, reliability and fault tolerance.

Course Objectives

1. To provide with the ability of web service programming.

2. To provide with the ability of enterprise integration programming.

3. To provide with the ability to develop multi-layer enterprise integrations.

4. To provide students with the fundamentals of enterprise integration deployment.

Recommended or Required Reading

K. Hammer, T. Timmerman, Fundamentals of Software Integration , Jones & Bartlett Publishers, 2007. ,G. Hohpe, B. Woolf, Enterprise Integration Patterns: Designing, Building, and Deploying Messaging Solutions , The Addison-Wesley Signature Series, 2003.

Learning Outcomes

Upon the completion of this course a student :

1. Use web service based programming techniques.

2. Know and compare different enterprise software integration approaches.

3. Analyze enterprise integration problems, design and implement solutions.

4. Demonstrate the ability to abstract in enterprise integration context.

5. Apply enterprise applicaton integration methods to real world problems.

Grading

Midterm: 30%

Research Presentation: 40%

Final: 30%

Revisiting database systems, data definition, relational algebra, disk management, file management, memory management, transaction management, record management, metadata management, query processing, parsing, planning, database server, indexing, query optimization

Course Objectives

To master the topic of data management in computer science field, to be able to produce parts of a database management system, to gain to skill of researching

Recommended or Required Reading

E. Sciore, Database Design and Implementation, Secon Edition, Wiley, 2020., M. Fisher, J. Ellis, J. Bruce, JDBC(TM) API Tutorial and Reference , 3rd Edition (Java Series), Addison Wesley, Reading, Massachisetts, 2006.

Learning Outcomes

1. To install SimpleDB database management system, customize it as required

2. To develop applications that realize all the functions of a database management system

3. To learn the approaches and methods behind the functions of a database management system

4. Finding, implementing and presening solution to an engineering problem by means of software development in a team

Grading

Midterm 30%

Research Presentation 30%

Final 40%

This course investigates principles of distributed database systems including design and architecture, security, integrity, query processing and optimization, transaction management, concurrency control, and fault tolerance. The course blends theory with practice in that each student will use distributed database concepts to develop a JDBC application and to implement a JDBC driver onto Web-based distributed databases.

Course Objectives

1.To enrich his technical background

2.To make him able to think analytically

3.To gain the skill of proposing solutions for a given need

4.To gain the skill of using new technologies.

Recommended or Required Reading

M. T. Ozsu and P. Valduriez, Principles of Distributed Database Systems , Springer, 3rd Edition, 2011

Learning Outcomes

Upon the completion of this course a student :

1. Learn design and planning principles of distributed database management systems

2. Gain the ability of creating and using distributed database

3. Learn the methods to provide consistency and security methods in distributed database management systems

4. Learn query optization in distributed databases in detail

5. Gain the skills to prepare projects as teams

Grading

Midterm: 30%

Research Presentation: 30%

Final: 40%

This course covers the topics of software quality management, quality management paradigms (CMMI, ISO, IEEE), methods used in quality management, enhancement of quality process, management of quality cost, error, preventive error avoidance.

Course Objectives

This course aims to teach software quality management within software development lifecycle.

Recommended or Required Reading

John W. Horch, “Practical Guide to Software Quality Management”, Addison Wesley, 2003.

Learning Outcomes

Ability to explain the components of Software Quality Assurance

Ability to apply quality components starting from the early stages of the project

Ability to measure the effectiveness of Software Quality Assurance processes

Ability to control the impact of Software Quality Assurance processes on the project budget and schedule

Introduction to the programming techniques to effectively utilize modern multicore computers. Identifying the parallelism, naming shared data, synchronizing threads, the latency and bandwidth associated with communication, analyzing & improving parallel performance, parallel programming tools, miscallenous lab works & exercises.

Course Objectives

To teach the parallel programming models.

To teach and analyzing the different parallel programming tools.

Teaching the parallelization methodologies by experimentally. To teach the analyzing methodology of a parallelized algorithm.

Recommended or Required Reading

Grama, A. Gupta, G. Karypis, V. Kumar, ‘Introduction to Parallel Computing’, Addison-Wesley, 2nd Ed., 2003 , Behrooz Parhami, ‘Introduction to Parallel Processing Algorithms and Architectures’, Plenum Series in Computer Science, 1999

Learning Outcomes

1. Analyze the requirements for the usage of parallel programming models and efficiently implement them.

2. Comparing different parallel programming methodologies and tools, then effectively take a decision which one will be used.

3. Efficiently analyzing and conversion of sequential algorithm to parallel algorithm.

4. Analyze the efficiency of a parallelized algorithm.

Grading

Midterm: 25%

Homework: 30%

Research Presentation: 20%

Final: 25%

An evolutionary approach to the multicore architectures, integration of multicore architectures with operating systems, OS kernel design for multiprocessors and multithreding, OS support for threads, User level threads, Kernel level threads, An example: Solaris threads, Threads and libraries, Hardware support for multithreading in a uniprocessor and in a multiprocessor.

Course Objectives

To teach the components and steps of the architecture design for multicore structures. To teach the contemporary operating systems and multicore architecture design models. To develop the critical thinking and analyzing for the relationship between operating system and multicore architecture design.

Recommended or Required Reading

U. Ramachandran, W. D. Leahy Jr.,’Computer Systems: An Integrated Approach to Architecture and Operating Systems’, Addison Wesley, 2010 ,C. Hughes,T. Hughes,’Professional Multicore Programming Design and Implementation for C++ Developers’, Wiley Publishing, Inc., 2008 ,B. Parhami, ‘Introduction to Parallel Processing Algorithms and Architectures’, Plenum Series in Computer Science, 1999.

Learning Outcomes:

1. Analyze and implement the design steps of computer architecture by using different multicore architecture models.

2. Analyzing the components of the operating systems for multicore architectures.

3. Analyzing hardware design problems of multicore architectures.

4. Analyzing the limits of solutions and restrictions of operating systems for different multicore architecture models.

Grading

Midterm: 30%

Presentation: 35%

Final: 35%

Finite state machine design and analysis; high-level hardware description languages, VHDL, automated synthesis in design; digital integrated circuit design and advanced design principles; electrical properties of digital circuits, synchronous and asynchronous circuits, computer arithmetic, and interfacing to external circuitry, digital system testing and design for testability; implementation of embedded computing systems in terms of Application Specific Integrated Circuits; Design for reuse.

Course Objectives

To give the students design issues of embedded system hardware and to introduce the latest technologies.

Recommended or Required Reading

Berger, Arnold. Embedded systems design:an introduction to processes tools and tecniques. San Francisco;Lawrence, Kan: CMP Books, c2002.

Learning Outcomes

Ability to analyze complex digital circuits

Ability to design complex digital circuits

Demonstrating the ability to use VHDL in hardware design

Having fundamental knowledge of digital signal processing

Ability to use design tools

Design and implementation of software for programmable embedded systems; software tools such as compilers, schedulers, code generators, and system-level design tools; data-flow and control models of computation and software synthesis for uniprocessor and multiprocessor architectures; synchronous/reactive languages and their mathematical properties; implementation of signal processing, communication and control algorithms using variety of technologies such as digital signal processors, microcontrollers, FPGAs, ASICs and real-time operating systems; real-time kernel design; software implemented fault-tolerance techniques.

Course Objectives

To give students the knowledge of latest technologies on software development for various types of embedded systems.

Recommended or Required Reading

Berger, Arnold. Embedded systems design:an introduction to processes tools and tecniques. San Francisco;Lawrence, Kan: CMP Books, c2002. ,Adamski, Marian Andrzej. Design of Embedded Control Systems, Boston, MA : Springer Science+Business Media, Inc., 2005. ,Tebbs, David. Real time systems: management and design, McGraw-Hill    London    1977.

Learning Outcomes:

1. Have knowledge on the requirements of embedded software development

2. Have knowledge on various standards and tools

3. Be able to analyze and design common algorithms used in various embedded systems

4. Be able to use the knowledge of software verification, validation and test on a real project

Grading

Midterm: 30%

Presentation: 30%

Final: 40%

Declarative programming; problem solving; knowledge representation; reasoning; acting logically; uncertainty; learning; communicating.

Course Objectives

– To help students to conceptualize information representation and reasoning methods

– To emphasize the relatioship between problem solving and logical behavior

– To form the background for development of sophisticated applications that are capable of learning and can reason under uncertainty

Recommended or Required Reading

Russell, Stuart; Norvig, Peter; 1995; “Artificial Intelligence – A Modern Approach”; Prentice-Hall; 0-13-103805-2 ,- Luger, George F.; 1993; “Artificial intelligence: structures and strategies for complex problem solving”; Benjamin-Cummings; 0-80534-785-2

Learning Outcomes

1. Comprehension of different artificial intelligence techniques used for simulation of human behavior
2. Interpretation of traditional artificial intelligence methods based on mathematical logic
3. Construction of intelligent behavior with respect to hybrid application of artificial intelligence methods
4. Demonstration of applications with the use result-oriented languages based and hybrid techniques

Grading

Midterm 30%

Homework 30%

Final 40%

This course introduces students to the many concepts and techniques in Artificial Intelligence (AI). Topics covered include: problem spaces and search, heuristic search techniques, predicate logic, game playing techniques, planning, learning, natural language processing, and machine perception

Course Objectives

1. Analysing cognitive models

2.Analysing interaction models

3.Develop sample smart systems

Recommended or Required Reading

Luger, George; Stubblefield, William; 2004; “Artificial Intelligence: Structures and Strategies for Complex Problem Solving”; The Benjamin/Cummings Publishing; ISBN 0805347801 ,Russell, Stuart; Norvig, Peter; 2003; “Artificial Intelligence – A Modern Approach”; Prentice-Hall; 0131038052 ,Giarratano, Joseph C.; Ritel, Gary D.; 2005; “Expert Systems – Principles and Programming”; Thompson Course Technology; 0534384471 ,Munakata, Toshinori; 2007; “Fundamentals of the New Artificial Intelligence Neural, Evolutionary, Fuzzy and More”; Springer; 9781846288395 ,Maedche, Alexander; 2002 ; “Ontology learning for the semantic web”; Kluwer Academic Publishing; 0792376560 ,Hjelm, Johan; 2001; “Creating the semantic Web with RDF: professional developer s guide”; John Wiley; ,Goertzel, Ben; Pennachin, Cassio; 2007; “Artificial General Intelligence”; Springer; 9783540237334 ,Nabiyev, Vasif V.; 2005; “Yapay Zeka”; Seçkin Yayıncılık; 9753479859

Learning Outcomes

1. Effectively use evolutionary computation in applications

2. Effectively use artificial neural networks in applications

3. Effectively use fuzzy logic in applications

4. By using these meta heuristics, provide applications with smart behaviours

Grading

Midterm: 24%

Quiz: 6%

Homework: 10%

Research Presentation: 30%

Final: 30%

An introduction to the machine learning with examples in different application areas. Bayesian decision theory. Supervised learning techniques. Model selection. Dimensionality reduction. Clustering. Support vector machines. Graphical models. Introduction to neural networks. Reinforcement learning.

Course Objectives

1. To form background related to the machine learning

2. To provide students with the ability of analyzing the machine learning problems and recognizing appropriate solution techniques

3. To develop the ability to implement proper solution algorithms to the problems given

Recommended or Required Reading

Ethem Alpaydın, Introduction to Machine Learning (2nd Edition), MIT Press, 2010. ,Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2006. ,Richard Duda, Peter Hart and David Stork, Pattern Classification, 2nd ed. John Wiley & Sons, 2001. ,Tom Mitchell, Machine Learning, McGraw-Hill, 1997.

Learning Outcomes

1. To be able to describe the fundamental concepts in machine learning

2. To be able to analyze the machine learning problems and identify appropriate solution techniques

3. To be able to apply proper algorithms using proper tools to solve the encountered machine learning problems

4. To be able to interpret the output of the implementations from algorithmic point of view

Grading

Midterm 30%

Homework 30%

Final 40%

Basic terminology, history & background, Symmetrical cryptosystems, DES-AES, DES-AES likes, Asymmetrical cryptosystems, primality, hashing, factorization based (RSA)- ECC-Lattice cryptosystems, cryptographic protocols & applications, secrecy, authentication, integrity-authenticity, digital signatures, standards.

Course Objectives

To teach the security functions. To teach that which security functions are realized by which cryptographic tools. Teaching the relationship between the historical evolution of the symmetrical/asymmetrical cryptosystems and the developments of the technological and social life. Teaching that how the measure of the security level for cryptographic solutions and how the implement the international standards. To teach the currently relevant security solutions of information and communication technologies.

Recommended or Required Reading

W.Trappe, L.Washington, ‘Introduction to Cryptography with Coding Theory’, Pearson, 2006 ,P. Gutman, ‘Cryptographic Security Architecture’, Springer, 2004 ,D. R. Stinson, ‘Cryptography, Theory and Practice’, Chapman & Hall/CRC Press, 2002 ,B. Schneider, ‘Applied Cryptography, Protocols, Algorithms, and Source Code in C’, John Wiley & Sons, 1996

Learning Outcomes

1. Analyze vulnerabilities, threats and which security functions are realized.

2. Selection, implementation and testing of cryptographic tools for essential security functions.

3. Objectively read the technological evolution and efficiently analyzing of new requirements for the cryptographic solutions.

4. Usage ability of the international standards for the developed cryptographic solutions.

5. Selection of the best solution and implementation ability for a cryptographic security solution depends on security requirements of domain.

Grading

Midterm: 25%

Homework: 30%

Presentation: 20%

Final: 25%

Link Encryption, End-to-End Encryption, Network and Internet Based Attacks, Firewalls, Email Security Issues, Web Based Security Issues, Disaster Recovery, C2 Functionality Stand-alone vs Network Operation, Hackers and Crackers, Network Crackings.

Course Objectives

1. To teach the fundamentals of computer network security.

2. To provide students with the fundementals of network package security.

3. To teach the fundementals of computer local/cooperate network security.

Recommended or Required Reading

w. Stallings, Network Security Essentials: Applications and Standards , Prentice-Hall, 2010. ,C. Kaufman, R. Perlman, M. Speciner, Network Security: PRIVATE Communication in a PUBLIC World , Prentice-Hall, 2002.

Learning Outcomes

1. Know and compare different network security approaches.

2. Analyze network security problems, design and implement solutions.

3. Demonstrate the ability to abstract in security context.

4. Apply security engineering methods to real world problems.

Grading

Midterm: 30%

Presentation: 40%

Final: 30%

Introduction to security of information and communication technologies, fundamental security functions and related cryptographic tools, network security and related problems, protection mechanisms and tool will be evaluated. Then current hot topic security problems will be analyzed for “cloud computing”, “web applications and services”, “wireless networks”, etc. and at last security management of information systems will be discussed. Students will have a term project for the course about an up-to-date problem of these topics, which they are interested in and will prepare a written report about it then, present their work.

Course Objectives

1. To teach the security problems of information and communication technologies and related solution tools.

2. To teach the open security problems and their risks.

3. Teaching the unsolved security problems and continued researches for current information and communication technologies.

4. Teaching the designing, building and managing of a secure information system.

Recommended or Required Reading

B. Schneider, ‘Applied Cryptography, Protocols, Algorithms, and Source Code in C’, John Wiley & Sons, 1996

Learning Outcomes

1. Having the competence to analyze security risks related to information and communication technologies.

2. Being able to select and test the appropriate tools for solving security problems.

3. Gaining the ability to follow technological developments and identify potential security risks and threats.

4. Possessing the fundamental knowledge and methodology to carry out the security design and management of information systems.

Turning theoretical ideas into solution sets in computer science. Integration of mathematical approaches with general problem solving techniques and computer science applications. Topics will be from Algorithms, Complexity Theory, Game Theory, Probability Theory, Graph Theory, Automata Theory, Algebra, and Cryptography.

Course Objectives

To teach the fundamentals topics and theorems of computer science. To teach the research, analyze, compare and modeling techniques of computer science. To teach the hot topic concepts, research areas and problems of computer science.

Recommended or Required Reading

The text book/books are determined by decided topics.

Learning Outcomes

1. Analyzing capability to find new requirements and problems of current application areas of computer scince

2. Defining and modelling capability of computer science hot topic research problems.

3. To have the vision and to be able to do an evaluation for the future career plans on computer science

4. To be able to apply fundamental theorems of computer science with interdisciplinary research and studies.

Grading

Midterm: 20%

Homework: 20%

Presentation: 40%

Final: 20%

Evolutionary computation methods, discrete and continuous optimization problems, software libraries for evolutionary computation, Genetic Algorithms, Genetic Programming, Evolution Strategies, Differential Evolution, Particle Swarm Optimization, Multi-objective optimization.

Course Objectives

This course aims to provide information about evolutionary computation techniques and their application to optimization problems.

Recommended or Required Reading

Introduction to Evolutionary Computing by A.E. Eiben and J.E. Smith, Springer, 2nd Edition Global Optimization Algorithms – Theory and Application –Thomas Weise, 2009 Essentials of Metaheuristics, Sean Luke, Second Edition, 2012

Learning Outcomes:

1. To be able to explain evolutionary computation and its main components.

2. To be able to identify the most appropriate evolutionary computation method to a given

problem.

3. To be able to apply evolutionary computation methods to optimization problems.

4. To be able to design experimental studies for evolutionary algorithms

Grading:

• Midterm %25
• Term Project %25
• Final  %50

Submissions: Project, Report

Supplementary course: CENG 462

Approach robotics mainly software- and application-oriented; inspect mechatronic parts and hardware architectures of robots; analyse software components and software architectures; analyse software architectures and their properties that might provide smart behaviours; analyse hardware and software requirements for collaborative robots; develop collaborative and smart appearing robot applications.

Course Objectives

1. Learn hardware properties of robots (CO 1)
2. Learn software properties of robots (CO 2;3)
3. Develop applications that provide robots with collaborative and smart behaviours (CO 2;3)

Recommended or Required Reading

Mataric, Maja J; 2007; “The Robotics Primer”; The MIT Press; ISBN 026263354x ,Craig, John. J.; 2003; “Introduction to Robotics: Mechanics and Control”; Prentice Hall; ISBN 0201543613 ,Jones, Joe; 2003; “Robot Programming: A Practical Guide to Behaviour-Based Robotics”; McGraw-Hill, ISBN 0071427783 ,Enderton, Herbert B.; 2001; “A Mathematical Introduction to Logic”; Academic Press; ISBN 0-12-238452-0 ,Kelly, John; 1997; “The Essence of Logic”; ISBN 0-13-396375-6 ,Bell,J.L.; 1997; “A course in mathematical logic”; 1997; N.H.; ISBN 0-7204-2844-0 ,Gabbay, Dov M.; 2000; “Temporal logic: mathematical foundations and computational aspects”; Oxford: Clarendon press; ISBN 0-19-853768-9 ,Lootsma, Freerk A.; 1997; “Fuzzy logic for planning and decision making”; Kluwer Academic Publishers; ISBN 0-7923-4681-5

Learning Outcomes

1. Distinguish robot types and behaviours

2. Use techniques for smart robot behaviours

3. Use techniques for collaborative robot behaviours

4. Develop robots from robotics kits

Grading

Midterm: 20%

Quiz: 10%

Homework: 1o%

Presentation: 20%

Final: 30%

Report: 10%

Knowledge discovery overview, data mining in general, data preparation, basics of data mining, association rule mining, classification and prediction, cluster analysis, web mining, data mining applications, term projects.

Course Objectives

1.To enrich his technical background by meeting a new area,

2.To gain the skill of making research,

3.To be able to understand a complex problem and to be able to propose a solution,

4.To be able to manage a technical process,

5.To learn effective oral and written communication.

Recommended or Required Reading

J. Han, Data Mining: Concepts and Techniques , Morgan Kaufman, 2000. ,M. H. Dunham, Data Mining Introductory and Advanced Topics , Prentice Hall, Pearson Education, 2003. ,R. J. Roiger and M. W. Geatz, Data Mining A Tutorial Based Primer , Addison Wesley, 2003. ,M. J. A. Berry and G. S. Linoff, Mastering Data Mining , Wiley, 2000

Learning Outcomes

1. Learn the process of knowledge discovery and data mining algorithms

2. Gain the ability of using data mining tools

3. Understand a data mining problem, design the process overflow and implement the steps

4. Document and present a Project

Grading

Midterm: 30%

Presentation: 30%

Final: 40%

In addition to an introductory treatment of business fundamentals in information technology, course topics will include paradigms, strategies and methods in goal setting, team formation, intellectual property, customer projects and business management for information technologies.

Course Objectives

This course aims to present an entrepreneurial perspective with particular emphasis on information technology. This course emphasizes the entrepreneurial mindset, which is necessary to create or grow economically viable enterprises in information technology business.

Recommended or Required Reading

Stephen Covey, The 7 Habits of Highly Effective People, Free Press, 2004. ,Thomas Friedman, The World Is Flat, 3.0, A Brief History of the Twenty-First Century, Picador, Paperback, 2007. ,Fuat Alican, Yol, 2010.

Learning Outcomes

1. To know entrepreneurship in information sciences industry.

2 To understand entrepreneur vision and thinking.

3 To be prepared to build business and products.

4 To understand the sustainable growth concept.

Grading

Midterm: 30%

Presentation: 40%

Final: 30%

This course covers rendering techniques and programming interfaces for Computer Graphics. The course topics include basic mathematical concepts in Computer Graphics (two and three dimensional transformations), lighting models, methods for basic scene modeling and visibility analysis. Application of these Computer Graphics techniques on modern hardware using OpenGL is also covered.

Course Objectives

The main aim of the course is to familiarize the students with basic concepts in Computer Graphics such as modeling, rendering, texturing, and lighting. The students will gain practice in applying their knowledge using standard programming interfaces such as OpenGL.

Recommended or Required Reading

Interactive Computer Graphics: A Top-Down Approach with OpenGL, by Edward Angel and Dave Shreiner

Learning Outcomes

1. A bility to apply computer graphics techniques using standard programming interfaces

2. Ability to derive the mathematical relationship between 3D objects and image representations

3. Ability to compare different lighting models

4. Ability to optimize rendering software using visibility data structures

This course covers advanced rendering techniques for Computer Graphics. The course topics include advanced lighting models, acceleration and volume rendering techniques. Acceleration of these Computer Graphics techniques on modern hardware is also covered.

Course Objective

The main aim of the course is to familiarize the students with advanced techniques in Computer Graphics topics such as modeling, rendering, texturing, and lighting. The students will also gain practice in applying their knowledge.

Recommended or Required Reading

Computer Graphics: Principles and Practice. 3rd Ed., J.F. Hughes et al.

Learning Outcomes

1. Apply advanced computer graphics techniques in practice

2. Derive the mathematical equations for illumination and volume rendering techniques

3. Compare different kinds of lighting models

4. Optimize rendering software

Grading

Midterm: 25%

Homework: 30%

Final: 35%