EUROPEAN CREDIT TRANSFER SYSTEM
INFORMATION PACKAGE
DEPARTMENT OF COMPUTER ENGINEERING
Gazi University Faculty of Engineering
Celal Bayar Bulvarı
06570 Maltepe-ANKARA,TURKEY
Tel.: + 90 312 582 3130
+ 90 312 230 6503
Fax: + 90 312 230 6503
+ 90 312 230 8434 (Engineering Faculty)
Web: http://mf-bm.gazi.edu.tr
DEPARTMENT
Chairman : Prof. Dr. M. Ali AKCAYOL
Tel.: + 90 312 582 3130
Fax: + 90 312 230 6503
E-mail: akcayol @gazi.edu.tr
ECTS COORDINATION
Tel.: + 90 312 582 3132
Fax: + 90 312 230 6503
E-mail: [email protected]
ECTS Coordinator: Assoc.Prof. Dr. Hasan Şakir BİLGE
2
GENERAL INFORMATION
Computer engineering is a branch of engineering concerned with design, development, and application of
computer systems. The mission of the Department of Computer Engineering is to produce and disseminate
theory, principles, practice, design, evaluation, and improvement of computing systems in the contexts of
computer hardware and software. The aim of Computer Engineering Department is to provide each of its
graduates a solid educational foundation leading to successful and sustainable career in computer
engineering. All graduates of the Computer Engineering program should have:
the analysis, design, implementation and documentation skills to qualify them for employment in
technical areas of Computer Engineering.
Communications and interpersonal skills to enable them to participate in interdisciplinary engineering
teams.
the skills, confidence, and experience to enable them to assume positions of technical leadership.
a solid foundation in basic mathematics, science, and computer engineering that will enable them to
continue their professional development for a life-long career in computer engineering.
Research and Laboratories
Ongoing Projects:
National IPv6 Project
New Aproaches to Data Security and Defence Strategies
Re-Structuring the Traffic Auditing and Accident Services And Determining the Locations of Regional
Traffic Statıons with Performance-Based Resource Management System
Development of Applications on Malware and Protection in Mobile Environment
Development of Security Aware Intelligent Routing Protocol for Broadband Wireless Mobile
Networks
Feature extraction by using 3D discrete cosine transform for face recognition
Completed Projects:
Artificial Intelligence Based Query Optimized Open Source XML Database Server Software (Gazi
University Research Foundation)
Artificial Intelligence Education and Application Development Laboratory (Gazi University Research
Foundation)
Development of Turkey Medical Information Network with GSM/GPRS based wireless network (Gazi
University Research Foundation)
Digital Processing of Ultrasound Images (Gazi University Research Foundation)
GSM Based Scada System Design and Application (Gazi University Research Foundation)
Image Processing Laboratory (Gazi University Research Foundation)
Intelligent Software Development for Information and Computer Security (Gazi University Research
Foundation)
Operating Systems Laboratory (Gazi University Research Foundation)
Smart Microphone for Mobile Devices (Gazi University Research Foundation)
The Network of Excellence for Innovative Production Machines and Systems (I*PROMS)
Web Based Mobile Robot for Scientific and Educational Purposes (supported by Science Partnership
Programme of British Council Turkey)
Laboratories:
3
Computer Laboratories: There are 36 personal computers with high-speed network connection. These
laboratories are used for courses and other purposes. Microsoft software (MSDN AA) are running on
computers.
Computer Network Laboratory: There are some computer network equipments in this laboratory, e.g.
ATM backbone switches, ATM network cards and fiber optics cables.
Digital Design Laboratory: There are 50 FPGA development kits, 3 personal computers, and necessary
software in this laboratory. This laboratory is used for applications of advanced digital design course.
Related research projects are being conducted in this laboratory.
Hardware Laboratory: Undergraduate students are learning internal hardware components of computers in
this laboratory.
Security Laboratory: This laboratory is used for applications of information and computer security course.
Related research projects are being conducted in this laboratory.
Wireless Communication Laboratory: There are GSM/GPRS modems, related software, programmer sets,
and many different GSM/GPRS antennas in this laboratory. Related research projects are being conducted
in this laboratory.
Degrees Granted
Bachelor of Science in Computer Engineering 4 years * (8 semesters)
Master of Science in Computer Engineering (with thesis) 2 years ** (4 semesters)
Philosophy of Doctorate Degree 4 years *** (8 semesters)
* The course of study may be extended to 7 years or 14 semesters ** The course of study may be extended another 2 semesters for students who meet the requirements of the Institute of Science and Technology.
*** The course of study may be extended another 4 semesters for students who meet the requirements of the Institute of Science and Technology.
Academic Staff and Research Areas
Prof.Dr. Şeref SAĞIROĞLU:
Applications of artificial neural networks, intelligent antenna analysis and design, fuzzy logic, heuristic
approaches, computer and information security, intelligent system modelling, identification and control,
web based technologies, robotics, steganography, digital signal and image processing, biometric systems,
e-signature and public key cryptograhy.
Prof.Dr. M. Ali AKCAYOL:
Fuzzy logic, artificial neural networks, genetic algorithm, hybrid intelligent systems, intelligent
optimization techniques, mobile wireless technologies, web technologies, microcontrollers, smartcards
Assoc.Prof.Dr. Suat ÖZDEMİR:
Computer networks, wireless networks, sensor networks, network security, information security
Image processing, face recognition, signal processing, array signal processing, beamforming, ultrasonic imaging, digital design with hardware description languages
Assist.Prof.Dr. Hacar KARACAN:
Software engineering, human computer interaction, database management systems, expert systems
Instructor Dr. Murat HACIÖMEROĞLU:
3-D computer graphics, crowd simulations, Java
Instructor Dr. Muhammet Ünal:
Assoc.Prof.Dr. Hasan Şakir BİLGE:
4
Wireless Networks and Wireless Network Security, Data Security and Encryption, Parallel and Distributed
Programming, Multi-core Programming, Supercomputers, Computer Architectures, Embedded Systems
Instructor Dr. Oktay Yıldız:
Data Mining, Machine Learning, Bioinformatics
Graduate Courses:
Course Code Course Title
5011329 ARTIFICIAL NEURAL NETWORKS
5021329 APPLIED ARTIFICIAL INTELLIGENCE
5031329 ADVANCED DIGITAL DESIGN
5041329 COMPUTER VISION
5051329 INFORMATION AND COMPUTER SECURITY
5061329 IMAGE PROCESSING
5071329 INTELLIGENT OPTIMIZATION TECHNIQUES
5081329 APPLICATIONS OF FUZZY SETS IN ENGINEERING
5091329 HYBRID INTELLIGENT SYSTEMS
5101329 MOBILE AND WIRELESS NETWORKS
5111329 ADVANCED SOFTWARE ENGINEERING
5131329 WIRELESS SENSOR NETWORKS
5141329 ENTERPRISE INFORMATION SECURITY
5151329 INTERACTIVE SYSTEMS DESIGN
5161329 NEW GENERATION INTERNET TECHNOLOGIES
5171329 NEW GENERATION COMMUNICATIONS TECHNOLOGIES
5181329 ADVANCED LOGIC CIRCUIT DESIGN
5191329 PATTERN RECOGNITION
5201329 DATA MINING
5211329 SEMANTIC WEB
5221329 3D GAME PROGRAMMING
5231329 WIRELESS NETWORK SECURITY
5241329 MACHINE LEARNING
5
Course Title-Course Code: ARTIFICIAL NEURAL NETWORKS - 5011329
Name of the Programme: DEPARTMENT OF COMPUTER ENGINEERING
Semester
Teaching Methods Credits
Lecture Recite Lab. Project Homework Other Total Credit ECTS Credit
2 42 15 - 112 19 - 188 3 7.5
Language Turkish
Compulsory / Elective
Elective
Prerequisites -
Course Contents
Concepts of Intelligence, Multiple Intelligence and Artificial Intelligence (AI). AI techniques: GA, TS, ES and ANNs. Concepts of ANNs. Structures: Multilayered perceptrons, hopfield networks, LVQ, RBFN. Training algorithms: Backpropagation, Genetic algorithm, Levenberg-Marquardt, Quickpropagation, Delta-Bar-Delta, Extended Delta-Bar-Delta, Directed Random Search. Applying techniques and methodologies of ANNs to industrial applications. ANN Research Application Projects.
Course Objectives
The purpose of this course is to provide the student with a clear presentation of the theory and application of the principles of artificial neural networks in computer science and to develop students’ ability to design ANN structure for problems.
Learning Outcomes and Competences
The main outcome of this course is to fullfil students with the skills of solving problems with the use of ANNs.
Textbook and /or References
1. Artificial Neural Networks: A Compherensive Foundation, S. Haykin, 1994. 2. Applications of Artificial Intelligence in Engineering I: Artificial Neural Networks, in Turkish, Ufuk Kitabevi, 2003.
Assessment Criteria
If
any,mark
as (X)
Percent
(%)
Midterm Exams x 30
Quizzes - -
Homeworks x 10
Projects x 60
Term Paper - -
Laboratory Work - -
Other - -
Final Exam - -
Instructors Prof.Dr. ġeref SAĞIROĞLU, [email protected]
Week
Subject
1 2 3 4 5 6 7 8
Introduction to Artificial Intelligence
Introduction to Artificial Intelligence Techniques
Basic terminology at Artificial Intelligence and history of AI
AI structures
AI learning Algorithms
Different AI applications
How we adapt AI to problem?
6
9 10 11 12 13 14
Research Homework
Application Homework
Presentation of research and Application homework
Presentation of research and Application homework
Presentation of research and Application homework
Presentation of research and Application homework
7
Course Title-Course Code: APPLIED ARTIFICIAL INTELLIGENCE - 5021329
Name of the Programme: DEPARTMENT OF COMPUTER ENGINEERING
Semester
Teaching Methods Credits
Lecture Recite Lab. Project Homework Other Total Credit ECTS Credit
2 42 15 - 112 19 - 188 3 7.5
Language Turkish
Compulsory / Elective
Elective
Prerequisites -
Course Contents
Concept of Intelligence and Artificial Intelligence and their techniques. Concepts of learning strategies, Problem solving and search strategies. Principles. Artificial Intelligence Tools. Knowledge Representation, Representation methods and techniques. Problem Analysis Techniques. Applications of LISP and PROLOG and their examples.
Course Objectives
The purpose of this course is to provide the student with a clear presentation of the theory and application of the principles of artificial intelligence in computer science and to develop students’ ability to design artificial intelligence structure for problems.
Learning Outcomes and Competences
The main outcome of this course is to fullfil students with the skills of solving problems with the use of artificial intelligence.
Textbook and /or References
Artificial Intelligence: A Modern Approach, S Russel, P. Norwig, Prentice Hall 2003.
Assessment Criteria
If
any,mark
as (X)
Percent
(%)
Midterm Exams x 30
Quizzes - -
Homeworks x 10
Projects x 60
Term Paper - -
Laboratory Work - -
Other - -
Final Exam - -
Instructors Prof.Dr. ġeref SAĞIROĞLU, [email protected]
Week
Subject
1 2 3 4 5 6 7 8 9 10
Concept of Intelligence and Artificial Intelligence and their techniques. Concepts of learning strategies, Problem solving and search strategies. Problem solving and search strategies. Principles. Artificial Intelligence Tools. Artificial Intelligence Tools. Artificial Intelligence Tools. Knowledge Representation, Representation methods and techniques. Knowledge Representation, Representation methods and techniques.
8
11 12 13 14
Problem Analysis Techniques. Problem Analysis Techniques. Applications of LISP and PROLOG and their examples. Applications of LISP and PROLOG and their examples.
9
Course Title-Course Code: ADVANCED DIGITAL DESIGN - 5031329
Name of the Programme: DEPARTMENT OF COMPUTER ENGINEERING
Semester
Teaching Methods Credits
Lecture Recite Lab. Project Homework Other Total Credit ECTS Credit
2 42 34 56 - 56 188 3 7.5
Language Turkish
Compulsory / Elective
Elective
Prerequisites -
Course Contents
Programmable logic devices (FPGA, CPLD), digital design with hardware description languages (Verilog, VHDL), synthesis, simulation, validation, programmable device implementation, embedded processor design.
Course Objectives
Teaching of digital design with hardware description languages, simulation, implementation on FPGAs.
Learning Outcomes and Competences
Learning of digital design with hardware description languages, simulation, implementation on FPGAs.
Textbook and /or References
1. Verilog HDL : a guide to digital design, Samir Palnitkar, 1996. 2. VHDL: analysis and modeling of digital systems, Zainalabedin Navabi, McGraw-Hill, 1998.
Assessment Criteria
If
any,mark
as (X)
Percent
(%)
Midterm Exams - -
Quizzes - -
Homeworks x 30
Projects - -
Term Paper - -
Laboratory Work x 30
Other - -
Final Exam x 40
Instructors Assist.Prof.Dr. Hasan ġ. BĠLGE, [email protected]
Week
Subject
1 2 3 4 5 6 7 8 9 10 11 12 13
Introduction Programmable logic devices (FPGA, CPLD), Programmable logic devices (FPGA, CPLD), Digital design with hardware description languages (Verilog, VHDL), Digital design with hardware description languages (Verilog, VHDL), Synthesis, Synthesis, Simulation, Simulation, Validation, Programmable device implementation, Programmable device implementation, Embedded processor design.
10
14 Embedded processor design.
11
Course Title-Course Code: COMPUTER VISION - 5041329
Name of the Programme: DEPARTMENT OF COMPUTER ENGINEERING
Semester
Teaching Methods Credits
Lecture Recite Lab. Project Homework Other Total Credit ECTS Credit
2 42 11 56 56 23 188 3 7.5
Language Turkish
Compulsory / Elective
Elective
Prerequisites -
Course Contents
Image formation, feature extraction, region growing, boundary detection, texture analysis, stereo vision, sequence of images, motion estimation, two-dimensional and three-dimensional representation, matching.
Course Objectives
Understanding the role of computer vision in real problems. Improving practical problem solving skills in computer vision.
Learning Outcomes and Competences
Finding appropiate solutions to complex vision problems.
Textbook and /or References
1. Computer Vision: A Modern Approach, David A. Forsyth, Jean Ponce, Prentice Hall, 2003. 2. Computer Vision, Linda G. Shapiro, George C. Stockman, Prentice Hall, 2001.
Assessment Criteria
If
any,mark
as (X)
Percent
(%)
Midterm Exams - -
Quizzes - -
Homeworks x 30
Projects x 30
Term Paper - -
Laboratory Work - -
Other - -
Final Exam x 40
Instructors Assist.Prof.Dr. Hasan ġ. BĠLGE, [email protected]
Week
Subject
1 2 3 4 5 6 7 8 9 10 11 12
Introduction Image formation, feature extraction, feature extraction, region growing, boundary detection, texture analysis, stereo vision, stereo vision, sequence of images, motion estimation, motion estimation,
12
13 14
two-dimensional and three-dimensional representation, matching.
13
Course Title-Course Code: INFORMATION AND COMPUTER SECURITY - 5051329
Name of the Programme: DEPARTMENT OF COMPUTER ENGINEERING
Semester
Teaching Methods Credits
Lecture Recite Lab. Project Homework Other Total Credit ECTS Credit
1 42 15 112 19 188 3 7.5
Language Turkish
Compulsory / Elective
Elective
Prerequisites -
Course Contents
Introduction to information, security and computer security. Security engineering. Techniques for achieving security. Cryptography. Symetric and asymetric algorithms. Digital signatures. Authentication and identification schemes. Public key Infrastructure. Intrusion detection. Formal models of computer security. Software protection. Security of electronic mail and the World Wide Web. Electronic commerce. Firewalls. Risk assessment. Standards in security. Research and application projects.
Course Objectives
Providing students to understand the theory and application of the principles of computer and information security in computer engineering and science. Let them to develop their own ability and awarness to design a secure environment in computer useage and installation.
Learning Outcomes and Competences
The main outcome of this course is to fullfil students with the skills of establishing secure electronic media protecting their own information.
Textbook and /or References
1. Security Engineering, R. Anderson, 0-471-38922-6, Willey, New York, 2001. 2. Cryptography And Network Security Principles And Practices" Stallings Will, Prentice Hall, 2003. 3. ―e-signature and PKI‖, Lecture Notes in Turkish, ġ. Sağıroğlu, 2005, Ankara.
Assessment Criteria
If
any,mark
as (X)
Percent
(%)
Midterm Exams x 30
Quizzes - -
Homeworks x 10
Projects x 60
Term Paper - -
Laboratory Work - -
Other - -
Final Exam - -
Instructors Prof.Dr. ġeref SAĞIROĞLU, [email protected]
Week
Subject
1 2 3 4 5 6
Introduction to Information, Security, and Computer Security
Security Engineering
Security Techniques
Cryptography Science
Symmetric and Asymmetric Algorithms
E-signature, Id verification techniques
14
7 8 9 10 11 12 13 14
Public key infrastructure
Attack Detection Systems, Firewalls
Computer security models and standards
Research and Application Projects
Research and Application Projects
Research and Application Projects
Research and Application Projects
15
Course Title-Course Code: IMAGE PROCESSING - 5061329
Name of the Programme: DEPARTMENT OF COMPUTER ENGINEERING
Semester
Teaching Methods Credits
Lecture Recite Lab. Project Homework Other Total Credit ECTS Credit
1 42 19 56 71 188 3 7.5
Language Turkish
Compulsory / Elective
Elective
Prerequisites -
Course Contents
Introduction to digital image processing. Digital image fundamentals, sampling and quantization. Image enhancement, histogram processing, filters. The Fourier transform and the frequency Domain. Image restoration, noise models. Color image processing. Image compression. Morphological image processing.
Course Objectives
Teaching digital image processing, applications of image processing methods. Encouraging related studies.
Learning Outcomes and Competences
Understanding digital image processing, choosing appropiate methods when solving newly encountered problems. Obtaining necessary background for further studies.
Textbook and /or References
1. Digital Image Processing, 2. Edition, R.C. Gonzalez, R.E. Woods, Prentice Hall, 2002. 2. Digital Image Processing Using MATLAB, R.C. Gonzalez, R.E. Woods, S.L. Eddins, Prentice Hall, 2004.
Assessment Criteria
If
any,mark
as (X)
Percent
(%)
Midterm Exams x 30
Quizzes - -
Homeworks x 30
Projects - -
Term Paper - -
Laboratory Work - -
Other - -
Final Exam x 40
Instructors Assist.Prof.Dr. Hasan ġ. BĠLGE, [email protected]
Week
Subject
1 2 3 4 5 6 7 8 9 10 11
Introduction to digital image processing. Digital image fundamentals, sampling and quantization. Image enhancement, Image enhancement, histogram processing, histogram processing, filters. The Fourier transform and the frequency Domain. Image restoration, noise models.
16
12 13 14
Color image processing. Image compression. Morphological image processing.
17
Course Title-Course Code: INTELLIGENT OPTIMIZATION TECHNIQUES - 5071329
Name of the Programme: DEPARTMENT OF COMPUTER ENGINEERING
Semester
Teaching Methods Credits
Lecture Recite Lab. Project Homework Other Total Credit ECTS Credit
1-2 42 50 38 58 188 3 7.5
Language Turkish
Compulsory / Elective
Elective
Prerequisites -
Course Contents
Applications of intelligent optimization techniques in complex engineering problems. Genetic algorithms, simulated annealing, fuzzy logic, neural networks, tabu search, and ant algorithm techniques. Examples to problem solving using these techniques.
Course Objectives
Teaching intelligent optimization techniques which are genetic algorithm, simulated annealing, fuzzy logic, neural networks, tabu search, and ant algorithm. Teaching how to use this intelligent optimization techniques in complex engineering problems.
Learning Outcomes and Competences
Learning intelligent optimization techniques which are genetic algorithm, simulated annealing, fuzzy logic, neural networks, tabu search, and ant algorithm. Learning how to use this intelligent optimization techniques in complex engineering problems.
Textbook and /or References
1. How to Solve It: Modern Heuristics 2nd ed. Revised and Extended, Michalewicz Zbigniew, Fogel David B., Springer-Verlag, 2004. 2. Intelligent Optimization Techniques, Pham, D.T., Karaboga, D., Springer Verlag, 1999. 3. Elements of Artificial Neural Networks, Kishan Mehrotra, Chilukuri K. Mohan and Sanjay Ranka, MIT Press, 1996.
Assessment Criteria
If
any,mark
as (X)
Percent
(%)
Midterm Exams x 35
Quizzes - -
Homeworks x 20
Projects - -
Term Paper - -
Laboratory Work - -
Other - -
Final Exam x 45
Instructors Prof.Dr. M. Ali AKCAYOL, [email protected]
Week
Subject
1 2 3 4 5 6 7 8 9 10
Introduction to Optimization Lineer Programming Lineer Programming Conventional Search Methods Conventional Search Methods Simulated Annealing Simulated Annealing Tabu Search Tabu Search Ant Algorithm
18
11 12 13 14
Ant Algorithm Genetic Algorithm Genetic Algorithm Neural Networks
19
Course Title-Course Code: APPLICATIONS OF FUZZY SETS IN ENGINEERING - 5081329
Name of the Programme: DEPARTMENT OF COMPUTER ENGINEERING
Semester
Teaching Methods Credits
Lecture Recite Lab. Project Homework Other Total Credit ECTS Credit
1 42 50 38 58 188 3 7.5
Language Turkish
Compulsory / Elective
Elective
Prerequisites -
Course Contents
Fuzzy set theory and fuzzy logic. Fuzzy operators and fuzzy relations. Applications of fuzzy sets in engineering. Examples to problem solving using fuzzy sets theory.
Course Objectives
Teaching fuzzy sets theory, fuzzy logic, fuzzy operators and fuzzy relations. Teaching fuzzy sets applications in engineering areas. Teaching examples to problem solving using fuzzy sets theory.
Learning Outcomes and Competences
Learning fuzzy sets theory, fuzzy logic, fuzzy operators and fuzzy relations. Learning fuzzy sets applications in engineering areas. Learning examples to problem solving using fuzzy sets theory.
Textbook and /or References
1. T.J.Ross, Fuzzy Logic with Engineering Applications, Addison Wesley, 1995. 2. Neuro-Fuzzy and Soft computing, Jiang, et al., Pearson Education, 1996. 3. Fuzzy Sets & Fuzzy Logic: Theory & Applications, George J. Klir , Bo Yuan, Pearson Education , 1995.
Assessment Criteria
If
any,mark
as (X)
Percent
(%)
Midterm Exams x 35
Quizzes - -
Homeworks x 20
Projects - -
Term Paper - -
Laboratory Work - -
Other - -
Final Exam x 45
Instructors Prof. Dr. M. Ali AKCAYOL, [email protected]
Week
Subject
1 2 3 4 5 6 7 8 9 10
Introduction
Classical Sets and Fuzzy Sets
Classical Sets and Fuzzy Sets
Classical Relations and Fuzzy Relations
Membership Functions
Membership Functions
Fuzzy-to-Crisp Conversions
Fuzzy Arithmetic
Classical Logic and Fuzzy logic
20
11 12 13 14
Fuzzy Rule-Based Systems
Fuzzy Control Systems
Other Engineering Applications
21
Course Title-Course Code: HYBRID INTELLIGENT SYSTEMS - 5091329
Name of the Programme: DEPARTMENT OF COMPUTER ENGINEERING
Semester
Teaching Methods Credits
Lecture Recite Lab. Project Homework Other Total Credit ECTS Credit
1 42 50 38 58 188 3 7.5
Language Turkish
Compulsory / Elective
Elective
Prerequisites -
Course Contents
Artificial neural networks-fuzzy systems, fuzzy systems-evolutionary algorithms, artificial neural networks-evolutionary algorithms, artificial neural networks-fuzzy systems-evolutionary algorithms, applications of hybrid systems, other special topics and application projects.
Course Objectives
Teaching hybrid intelligent systems. Teaching examples to problem solving using hybrid intelligent systems.
Learning Outcomes and Competences
Learning hybrid intelligent systems. Learning examples to problem solving using hybrid intelligent systems.
Textbook and /or References
1. Jang, J.S.R, Sun, C.T., Mizutani, E., "Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence", Pearson Education, 1996. 2. Goonatilake, S., Khebbal, S., "Intelligent Hybrid Systems", John Wiley & Sons Ltd, 1995.
3. Fuller, R., "Introduction to Neuro-Fuzzy Systems", Springer-Verlag, 2000.
4. Da Ruan, "Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms", Kluwer Academic Publishers, 1997.
Assessment Criteria
If
any,mark
as (X)
Percent
(%)
Midterm Exams x 35
Quizzes - -
Homeworks x 20
Projects - -
Term Paper - -
Laboratory Work - -
Other - -
Final Exam x 45
Instructors Prof. Dr. M. Ali AKCAYOL, [email protected]
Week
Subject
1 2 3 4 5 6
Introduction
Neural Networks –Fuzzy Systems
Neural Networks –Fuzzy Systems
Fuzzy Systems- Evolutionary Algorithms
Fuzzy Systems- Evolutionary Algorithms
22
7 8 9 10 11 12 13 14
Neural Networks- Evolutionary Algorithms
Neural Networks- Evolutionary Algorithms
Neural Networks-Fuzzy Systems - Evolutionary Algorithms
Neural Networks-Fuzzy Systems - Evolutionary Algorithms
Neural Networks-Fuzzy Systems - Evolutionary Algorithms Hybrid systems Applications
Neural Networks-Fuzzy Systems - Evolutionary Algorithms Hybrid systems Applications
Term Projects
23
Course Title-Course Code: MOBILE AND WIRELESS NETWORKS - 5101329
Name of the Programme: DEPARTMENT OF COMPUTER ENGINEERING
Semester
Teaching Methods Credits
Lecture Recite Lab. Project Homework Other Total Credit ECTS Credit
1-2 42 50 38 58 188 3 7.5
Language Turkish
Compulsory / Elective
Elective
Prerequisites -
Course Contents
Fundamental techniques in design of second generation wireless networks: cellular network and protocols, access techniques, signaling and mobility management, wireless data processing, mobile internet and personal communication services (PCS). Third generation wideband systems, novel technologies.
Course Objectives
Teaching fundamental techniques in design of second generation wireless networks, cellular network and protocols, access techniques, signaling and mobility management, wireless data processing, mobile internet and personal communication services (PCS). Teaching third generation wideband systems, novel technologies.
Learning Outcomes and Competences
Learning fundamental techniques in design of second generation wireless networks, cellular network and protocols, access techniques, signaling and mobility management, wireless data processing, mobile internet and personal communication services (PCS). Learning third generation wideband systems, novel technologies.
Textbook and /or References
(1) Stallings, W., ―Wireless Communications & Networks (2nd Edition)‖, Prentice Hall, 2004. (2) Rappaport, T., ―Wireless Communications: Principles and Practice (2nd Edition)‖, Prentice Hall, 2002. (3) Haykin, S., Moher, M., ―Modern Wireless Communications‖, Prentice Hall, 2004. (4) Schiller, J., ―Mobile Communications Second Edition‖, Addison Wesley, 2003.
Assessment Criteria
If
any,mark
as (X)
Percent
(%)
Midterm Exams x 35
Quizzes - -
Homeworks x 20
Projects - -
Term Paper - -
Laboratory Work - -
Other - -
Final Exam x 45
Instructors Prof. Dr. M. Ali AKCAYOL, [email protected]
Week
Subject
1 2 3 4 5
Introduction Fundamental techniques in design of second generation wireless networks Cellular network and protocols Cellular network and protocols Access techniques
24
6 7 8 9 10 11 12 13 14
Access techniques Signaling and mobility management Wireless data processing Wireless data processing Mobile internet and personal communication services (PCS) Mobile internet and personal communication services (PCS) Third generation wideband systems, Third generation wideband systems, Novel technologies
25
Course Title-Course Code: ADVANCED SOFTWARE ENGINEERING - 5111329
Name of the Programme:
DEPARTMENT OF COMPUTER ENGINEERING
Semester
Teaching Methods Credits
Lecture Recite Lab. Project Homework Other Total Credit ECTS Credit
1-2 42 50 38 58 188 3 7.5
Language Turkish
Compulsory / Elective
Elective
Prerequisites -
Course Contents
Concepts of software engineering: life cycle, planning, planning, realizing and test. Planning and realizing of large software systems. Determining software steps. Forming modular software structure. Coding principles. Planning tests. Maintenance of software. Management of large software projects. Real software examples. Term project.
Course Objectives
Teaching concepts of software engineering: life cycle, planning, planning, realizing and test. Teaching planning and realizing of large software systems. Teaching determining software steps and forming modular software structure. Teaching coding principles, planning tests and maintenance of software. Teaching management of large software projects. Teaching how to develop application projects.
Learning Outcomes and Competences
Learning concepts of software engineering: life cycle, planning, planning, realizing and test. Learning planning and realizing of large software systems. Learning determining software steps and forming modular software structure. Learning coding principles, planning tests and maintenance of software. Learning management of large software projects. Learning how to develop application projects.
Textbook and /or References
(1) Daniel H. Steinberg, Daniel W. Palmer, ―Extreme Software Engineering: A Hands-On Approach‖, Pearson Prentice Hall, 2004 (2) Kent Beck, ―eXtreme Programming Explained‖, Addison-Wesley, 1999. (3) Martin Fowler, Kent Beck, John Brant, William Opdyke, Don Roberts, Refactoring: Improving the Design of Existing Code, Addison-Wesley, 1999.
Assessment Criteria
If
any,mark
as (X)
Percent
(%)
Midterm Exams x 35
Quizzes - -
Homeworks x 20
Projects - -
Term Paper - -
Laboratory Work - -
Other - -
Final Exam x 45
Instructors Prof. Dr. M. Ali AKCAYOL, [email protected]
Week
Subject
1 2
Introduction Concepts of software engineering
26
3 4 5 6 7 8 9 10 11 12 13 14
Life cycle Planning Software Requirements Software Design Software Development Test Methods Maintenance of software Management New Approach at Software Engineering Real software examples. Term project Term project
27
Course Title-Course Code: WIRELESS SENSOR NETWORKS - 5131329
Name of the Programme: DEPARTMENT OF COMPUTER ENGINEERING
Semester
Teaching Methods Credits
Lecture Recite Lab. Project Homework Other Total Credit ECTS Credit
1 30 10 110 38 - 188 3 7.5
Language Turkish
Compulsory / Elective
Elective
Prerequisites None
Course Contents
This course will provide a comprehensive introduction to sensor networks, including the understanding of their unique characteristics and research challenges. Protocols at different network layers and their applications. Sensor network security. Data aggregation and false data detection in sensor networks.
Course Objectives
The purpose of this course is to introduce sensor networks to the students by surveying the state-of-the-art on sensor networks research so that the number of computer engineers in Turkey who are familiar with sensor networks is increased.
Learning Outcomes and Competences
The main outcome of this class is to introduce sensor networks to the students. Another important goal of the class is to train students to read research papers with a critical perspective.
Textbook and /or References
1. Sensor Network Operations, S. Phoha, T.F. La Porta, and C. Griffin (eds), pp. 422-441, ISBN: 0471719765, Wiley-IEEE Press, May 2006.
2. Security in Distributed, Grid, Mobile and Pervasive Computing", Edited by Prof. Yang Xiao, Auerbach Publications, CRC Press 2007.
3. Wireless Sensor Networks: An Information Processing Approach by Feng Zhao and Leonidas Guibas, Morgan Kaufmann Publishing (July 6, 2004), ISBN-10: 1558609148
Assessment Criteria
If
any,mark
as (X)
Percent
(%)
Midterm Exams x 20
Quizzes - -
Reading reports (Homework) x 40
Projects x 40
Term Paper - -
Laboratory Work - -
Other - -
Final Exam - -
Instructors Assist.Prof.Dr. Suat ÖZDEMĠR, [email protected]
Week
Subject
1 2 3 4 5 6 7
Introduction and overview Applications Sensor and network architecture
Deployment and organization Transport protocols Routing and data dissemination protocols Localization and tracking protocols
28
8 9 10 11 12 13 14
Medium access protocols Data storage protocols Data aggregation protocols Security protocols Secure data aggregation protocols Research and application projects Research and application projects
29
Course Title-Course Code: ENTERPRISE INFORMATION SECURITY - 5141329
Name of the Programme: DEPARTMENT OF COMPUTER ENGINEERING
Semester
Teaching Methods Credits
Lecture Recite Lab. Project Homework Other Total Credit ECTS Credit
1 42 14 112 19 31 188 3 7.5
Language Turkish
Compulsory / Elective
Elective
Prerequisites None
Course Contents
Intro to Enterprise Information Security, up-to-date developments, encryption and decryption techniques and approaches, often faced vulnerabilities, Enterprise Information Security standards, ISO 17799, CC 15408, ISO 2700X, Evaluating information assests and risk managements, Security for Enterprise Networks and applications, penetration for Enterprise Networks, Enterprise Information Security and social engineering, tests for Enterprise Information Security, application and research projects.
Course Objectives
The purpose of this course is to provide the students with a clear presentation of the theory and application of the principles of Enterprise Information Security in computer science and
to develop students’ ability to improve their perceptions in understanding and applying Enterprise Information Security.
Learning Outcomes and Competences
The main outcome of this course is to fullfil students with the skills of understanting Enterprise Information Security and its technologies and use those in solving security
problem.
Textbook and /or References
1. Cole, E., Krutz, R., Conley, J.W., ―Security Assessments, Testing, and Evaluation‖, Network Security Bible, Wiley Publishing Inc., Indianapolis, 607-612 (2005).
2. Abrams, D., M., ―FAA System Security Testing and Evaluation‖, Mitre Center for Advanced Aviation System Development McLean, Virginia (2003).
3. Layton, P., T., ―Penetration Studies – A Technical Overview‖, SANS Institute 2002. 4. Mathew, T., ―Ethical Hacking and Countermeasures EC-Council E-Business
Certification Series‖ Copyright © by EC-Council Developer Publisher OSB Publisher ISBN No 0972936211.
5. Klevinsky, T., J., Laliberte, S., Gupta, A., ―Hack I.T.: Security Through Penetration Testing‖, Publisher: Addison Wesley First Edition February 01, 2002ISBN: 0-201-71956-8, 544 pages.
6. Bilgi Teknolojisi— ―Bilgi Güvenliği için uygulama prensibi TS ISO/IEC 17799 Standartı‖ Türk Standartları Enstitüsü, 2005
7. Cryptography And Network Security Principles And Practices" Stallings Will, Prentice Hall, 2003.
8. Security Engineering, R. Anderson, Wiley, New York, 2001 9. ġ. Sağıroğlu, M. Alkan, Her Yönüyle Elektronik Ġmza, Grafiker Yayınları, 2006,
Ankara.
Assessment Criteria
If
any,mark
as (X)
Percent
(%)
Midterm Exams x 30
Quizzes - -
Reading reports (Homework) x 10
Projects x 60
Term Paper - -
Laboratory Work - -
Other - -
30
Final Exam - -
Instructors Prof. Dr. ġeref Sağıroğlu, [email protected]
Week
Subject
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Intro to Enterprise Information Security, Enterprise Information Security and Social Engineering, Up-to-date developments, often faced vulnerabilities, Evaluating information assests and risk managements, Enterprise Information Security standards, ISO 17799, CC 15408, ISO 2700X, and other standarts Penetration for Enterprise Networks, tests for Enterprise Information Security, Information Security Management Systems and Applications Information Security Management Systems and Applications Information Security Management Systems and Applications Applications of Enterprise Information Security, Application and research projects. Application and research projects.
Application and research projects.
31
Course Title-Course Code:
INTERACTIVE SYSTEMS DESIGN–5151329
Name of the Programme:
DEPARTMENT OF COMPUTER
ENGINEERING
Semester
Teaching Methods Krediler
Lecture Recite Lab. Project Homework Other Total Credit ECTS
Credit
1 – 2 42 - - 146 - - 188 3 7,5
Language Turkish
Compulsory
/
Elective
Elective
Prerequisites -
Course
Contents
Interactive systems, user-centered design, perception and memory, navigation, task analysis,
design principles, iterative design cycle, user experiments, future design principles.
Course
Objectives
Develop a theoretical and empirical understanding of user-centered design of computer
interfaces, and their uses,
Develop valid and reliable usability evaluation plans for any information technology
Provide an understanding of the social, psychological, and ethical issues associated with
interactive systems design
Offer a set of first-hand experiences which augment conceptual understanding of course
content.
Learning
Outcomes
and
Competences
Gaining the ability to handle software and hardware engineering problems from a
different point of view with the help of the theoretical information about interactive
systems,
Evaluating Computer Engineering outcomes by considering the human factor,
Adapting a user-centered point of view on new technology development stages,
Understanding the structure of processes and different views on interactive system
design,
Conducting different usability tests for computer systems,
Designing innovative interactive systems.
Textbook
and
/or
References
Barnum, C.M. (2002). Usability Testing and Research. New York : Longman
Benyon, D. (2005).Designing interactive systems :people, activities, contexts, technology. New York : Addison-Wesley
Selected papers.
Assessment
Criteria
If
any,mark
as (X)
Percent
(%)
Midterm Exams X 30
Quizzes
Homeworks
Projects X 30
Term Paper
Laboratory Work
Other
Final Exam X 40
Instructors Assist.Prof.Dr. Hacer Karacan
32
Week Subject
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Introduction
Human Perception and Mind Model
Requirements Analysis
Interactive Interface Design Theories
Interactive Web Design
Distance Education Systems Design
Virtual Reality Systems Design
Computer Systems Design and Evaluation Processes
Intelligent Devices
Usability Tests
Research on Interactive Systems
Project Presentations
Project Presentations
Project Presentations
33
NEW GENERATION INTERNET
TECHNOLOGIES - 5161329 DEPARTMENT OF COMPUTER ENGINEERING
Semester
Methods of Education Credits
Lecture Recit Lab. Other Total Credit ECTS
Credit
2 20 12 - 10 42 0 7.5
Language Turkish
Compulsory /
Elective Compulsory
Prerequisites -
Course
Contents
Introduction to Internet Communications, Voice Over IP, New Generation IP Technologies,
IPv6 Technologies, IPv6 Applications, IPv6 and Turkey Infrastructure, Internet ve Mobile
Communications Technologies, Power Line Communications, Cable TV and Internet
Applications, Wireless Technologies, MultiLanguages Domain Names, Next Generation
Domain Name
Course
Objectives
The purpose of this course is to provide the student with a clear presentation of the theory
and application of the principles of new generation of communications technologies in
computer science and to develop students’ ability to improve their perceptions in new
Technologies.
Learning
Outcomes
and
Competences
The main outcome of this course is to fullfil students with the skills of understanting new
generation communication technologies and use those in problem solving.
Textbook and
/or
References
1. IPv6 Essentials, by Silvia Hagen
2. Understanding IPv6 by Joseph Davies
3. IPv6 Network Administration by David Malone
4. Cisco Self-Study: Implementing Cisco IPv6 Networks (IPV6) by Regis Desmeules
5. Migrating to IPv6: A Practical Guide to Implementing IPv6 in Mobile and Fixed
Networks by Marc Blanchet
6. IPv6, Second Edition: Theory, Protocol, and Practice, 2nd Edition (The Morgan
Kaufmann Series in Networking) by Pete Loshin
7. Wireless Communication Technology, by Roy Blake
8. ADSL, VDSL, and Multicarrier Modulation
by John A. C. Bingham
9. Technologies for Next Generation Communications
Kenneth J. Turner (Editor), Evan H. Magill (Editor), David J. Marples (Editor)
Assessment Criteria
If
any,mark
as (X)
Percent
(%)
Midterm Exams X 30
Quizzes
Homeworks X 10
Projects X 60
Term Paper
Laboratory Work
Other
Final Exam
34
Instructors Assoc. Prof. Dr. Mustafa ALKAN
Weeks
Subjects
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Introduction to Internet Communications
Voice Over IP
New Generation IP Technologies
IPv6 Technologies
IPv6 Applications
IPv6 and Turkey Infrastructure
Internet ve Mobile Communications Technologies
Power Line Communications
Cable TV and Internet Applications
Wireless Technologies
New Generation Domain Names
Native Domain Names
MultiLanguages Domain Names
Next Generation Technologies
35
NEW GENERATION COMMUNICATIONS TECHNOLOGIES - 5171329
DEPARTMENT OF COMPUTER ENGINEERING
Semester
Methods of Education Credits
Lecture Recit Lab. Other Total Credit ECTS Credit
2 20 12 - 10 42 0 7.5
Language Turkish
Compulsory / Elective
Compulsory
Prerequisites -
Catalog Description
Communications Technologies, Multimedia Data and Multimedia Communications, New Broadbant Communications Services, Advanced Communications Systems, GSM (Global System for Mobile Communications), ISDN-(Integrated Services Digital Network), xDSL (Digital Subsriber Line), UMTS, (Universal Mobile Telecommunications System), W-CDMA ve CDMA 2000, (Wideband Code Division Multiple Access), PDC ( Personel Digital Communication), HSCSD (High-Speed, Circuit-Switched Data), (Wireless Broadband Access Technologies) D-AMPS (Digital Advanced Mobile Phone System), WIMAX: Worldwide Ġnteroperability for Microwave Access, GPRS; General Packet Radio Service, EDGE; Enhanced Data GSM Environment,
Course Objectives
The purpose of this course is to provide the student with a clear presentation of the theory and application of the principles of new generation of communications technologies in computer science and to develop students’ ability to improve their perceptions in new Technologies.
Course Outcomes
The main outcome of this course is to fullfil students with the skills of understanting new generation communication technologies and use those in problem solving.
Textbook and /or
References
1.Multimedia Computer Communications Technologies Chwan Hwu Wu- J.David Irwin 2001, 2. Communicatinos Systems Simon Haykin 2003. 3. The Handbook of Multimedia Information Management 4. The Business of WĠMAX Pareek D. 2005 5. ISDN and SS7 Architctures for Digital Signaling Networks Uyless, B. 2002 6. DSL Global Solution For Ġnteractive Broadband Kingdom, S. 2005
Assessment Criteria
Quantity Percentage
Midterm Exams 1 30
Quizzes - -
Homeworks 7 10
Projects 2 60
Term Paper - -
Laboratory Work - -
Other - -
Final Exam - -
Course Category by Content (%)
Mathematics and Basic Sciences 30
Engineering Science 30
Engineering Design 20
Social Sciences 20
36
Instructors Assoc. Prof. Dr. Mustafa ALKAN
Courses
Weekly program
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Communications Technologies,
Multimedia Data and Multimedia Communications,
New Broadbant Communications Services,
Advanced Communications Systems,
GSM (Global System for Mobile Communications),
ISDN-(Integrated Services Digital Network),xDSL (Digital SubsriberLine),
UMTS, (Universal Mobile Telecommunications System),
W-CDMA ve CDMA 2000, (Wideband Code Division Multiple Access),
PDC ( Personel Digital Communication),
HSCSD (High-Speed, Circuit-Switched Data),
Wireless Broadband Access Technologies
D-AMPS (Digital Advanced Mobile Phone System),
WIMAX: Worldwide İnteroperability for Microwave Access,
GPRS; General Packet Radio Service, EDGE; Enhanced Data GSM Environment,
37
Course Title-Course Code: ADVANCED LOGIC CIRCUIT DESIGN - 5181329
Name of the Programme: DEPARTMENT OF COMPUTER ENGINEERING
Semester
Teaching Methods Credits
Lecture Recite Lab. Project Homework Other Total Credit ECTS Credit
1 42 34 56 - 56 188 3 7.5
Language Turkish
Compulsory / Elective
Elective
Prerequisites -
Course Contents
General concepts and Boolean algebra; Equivalence relations and Lattice structures; State reduction in completely specified sequential machines; Design of synchronous and asynchronous sequential circuits; State assignment in asynchronous sequential circuits; race-free state assignment methods and Fault tolerant analysis in logic circuits; Programming Languages using in Logic circuits; Programmable logic circuit components (SPLD, CPLD, FPGA); Digital design with Field Programmable Gate Arrays; Logic circuit design with Programmable logic controllers; Very large scale integrated logic circuits.
Course Objectives
Approaches and methods related to the design of asynchronous sequential circuits.
Learning Outcomes and Competences
State reduction in completely specified sequential machines. State reduction in incompletely specified sequential machines. State assignment in synchronous sequential circuits. Partitioning of sequential circuits. Design of asynchronous sequential circuits.
Textbook and /or References
1. Lojik devreleri : (ArdıĢıl devreler) , Emin Ünalan, Ġstanbul : ĠTÜ, 1993. 2. Bilgisayar Mantık Devreleri Sayısal Sistem Tasarımı, Bülent Sankur, Yorgo
Istefanopulos, Boğaziçi Üniversitesi Döner Sermaye, 1994. 3. Bilgisayar Sistemleri Mimarisi, M. Morris Mano, Literatür Yayınları, Ġstanbul, Ekim 2002. 4. Maxfield C., ―Design Warriors Guide to FPGA‖, Mentor Graphics Corporation and Xilinx,
Inc., 2004. 5. S. Brown, Z. Vranesic, Fundamentals of Digital Logic with VHDL Design, McGraw-Hill,
2000. 6. FPGA Architecture, Altera, 2006.
Assessment Criteria
If
any,mark
as (X)
Percent
(%)
Midterm Exams x 30
Quizzes - -
Homeworks x 30
Projects - -
Term Paper - -
Laboratory Work - -
Other - -
Final Exam x 40
Instructors Prof.Dr. Etem Köklükaya, [email protected]
Week
Subject
38
1 2 3 4 5 6 7 8 9 10 11 12 13 14
General concepts and Boolean algebra Equivalence relations and Lattice structures State reduction in completely specified sequential machines State assignment in asynchronous sequential circuits Design of synchronous sequential circuits Design of asynchronous sequential circuits Race-free state assignment methods Fault tolerant analysis in logic circuits Midterm Exam Programs, hardware languages and application tools Digital design with Field Programmable Gate Arrays Programmable logic circuit components (SPLD, CPLD, FPGA) Logic circuit design with Programmable logic controllers Very large scale integrated logic circuits.
39
Course Title-Course Code: PATTERN RECOGNITION - 5191329
Name of the Programme: DEPARTMENT OF COMPUTER ENGINEERING
Semester
Teaching Methods Credits
Lecture Recite Lab. Project Homework Other Total Credit ECTS Credit
2 42 34 56 56 188 3 7.5
Language Turkish
Compulsory / Elective
Elective
Prerequisites -
Course Contents Classifiers based on Bayes decision theory, linear classifiers, non linear classifiers, feature
extraction, feature selection, dimensionality reduction, clustering.
Course Objectives
Understanding pattern recognition methods, obtaining the ability of effective use of feature selection and dimensionality reduction.
Learning Outcomes and Competences
Applying of classification methods in a sample problem successfully, obtaining the ability of effective use of feature selection and dimensionality reduction, understanding that pattern recognition can be applied to different problems in a similar way.
Textbook and /or References
1. Pattern Recognition, S. Theodoridis, K. Koutroumbas, Academic Press, 2008. 2. Pattern Classification, R.O. Duda, P.E. Hart, D.G. Stork, Wiley, 2000.
Assessment Criteria
If
any,mark
as (X)
Percent
(%)
Midterm Exams - -
Quizzes - -
Homeworks x 30
Projects x 30
Term Paper - -
Laboratory Work - -
Other - -
Final Exam x 40
Instructors Assist.Prof.Dr. Hasan ġ. BĠLGE, [email protected]
Week
Subject
1 2 3 4 5 6 7 8 9 10 11 12
General introduction Classifiers based on Bayes decision theory Linear classifiers Linear discriminant functions Non linear classifiers Support vector machines Feature extraction Feature extraction Linear transformations Feature selection Feature selection Dimensionality reduction
40
13 14
Clustering Project presentations
41
Course Title-Course Code:
DATA MINING - 5201329
Name of the Programme: DEPARTMENT OF COMPUTER ENGINEERING
Semester Teaching Methods Credits
Lecture Recite Lab. Project HW Other Total Credit ECTS
Credit
Spring 42 34 - 56 - 56 188 3 7,5
Language Turkish
Compulsory /
Elective Elective
Prerequisites N one
Course Contents
Introduction to data mining, application areas of data mining. Stages of data mining process.
Exploring Data, Preprocessing of data, Classification: Basic Concepts, Decision Trees, and
Model Evaluation, Association Analysis: Basic Concepts and Algorithms, Cluster Analysis:
Basic Concepts and Algorithms, Anomaly Detection, Web mining, Stream Data Mining
Course
Objectives
The purpose of this course is to introduce data mining concepts to graduate students. By
learning the fundamental concepts, techniques and algorithms of data mining students are
expected to be able to design, develop, and use real world data warehouses.
Learning
Outcomes and
Competences
The main outcome of this class is to have students with knowledge data mining techniques
that are useful in real world applications.
Textbook and
/or References
• Jiawei Han, Micheline Kamber, Data Mining: Concepts and Techniques,
Morgan Kaufmann, Data mining, ISBN 1558604898,2006
• Ian H. Witten , Eibe Frank, Data Mining: Practical Machine Learning Tools
and Techniques, Second Edition (Morgan Kaufmann Series in Data Management
Systems), 2005
• Pang-Ning Tan, Michael Steinbach, Vipin Kumar (2005). Introduction to Data
Mining. Addison Wesley, ISBN: 0-321-32136-7
Assessment
Criteria If any, mark
as (X) (%)
Midterm Exams X 30
Quizzes - -
Homework - -
Projects X 30
Term Paper - -
Laboratory Work - -
Others - -
Final Exam X 40
Instructor Assist. Prof. Dr. Suat Özdemir
Week Subject
42
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Introduction and overview of data mining
Application areas of data mining
Data warehouses and OLAP technology
Stages of data mining
Data and data preprocessing
Association rule analysis
Association rule analysis
Prediction and classification
Supervised learning: Classification algorithms
Unsupervised learning: Clustering algorithms
Unsupervised learning: Clustering algorithms
Data mining in complex data
Web mining
Stream data mining
43
Course Title-Course Code:
SEMANTIC WEB – 5211329
Name of the Programme:
DEPARTMENT OF COMPUTER ENGINEERING
Semester
Teaching Methods Krediler
Lecture Recite Lab. Project Homework Other Total Credit ECTS
Credit
1 – 2 42 - - 146 - - 188 3 7,5
Language Turkish
Compulsory /
Elective Elective
Prerequisites -
Course
Contents
Simple Ontologies in RDF and RDF Schema, RDF Formal Semantics, Ontologies in OWL, OWL
Formal Semantics, Ontologies and Rules, Query Languages, Ontology Engineering, Logic and Inference
Rules, Applications.
.
Course
Objectives
Develop a theoretical and empirical understanding of standardized knowledge representation
languages for modeling ontologies operating at the core of the semantic web
Offer a set of first-hand experiences which augment conceptual understanding of course content.
Learning
Outcomes and
Competences
Understanding the computational aspects of Information Extraction (IE) and Integration from
unstructured and semi-structured sources
Gaining the ability to build domain-specific Semantic Search Engines to improve Web
Searching
Designing and conducting different applications on course content
Textbook and
/or References
Hitzler, P., Krötzsch, M. & Rudolph, S. (2009). Foundations of Semantic Web Technologies,
Chapman & Hall/CRC.
Antoniou, G. & Van Harmelen, F. (2008). A semantic Web primer. Cambridge, Mass. : MIT
Press
Selected papers.
Assessment
Criteria
If
any,mark
as (X)
Perce
nt
(%)
Midterm Exams X 30
Quizzes
Homeworks
Projects X 30
Term Paper
Laboratory Work
Other
Final Exam X 40
Instructors Assist.Prof.Dr. Hacer Karacan
Week Subject
44
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Introduction
Structured Web Documents in XML
Simple Ontologies in RDF and RDF Schema
RDF Formal Semantics
Web Ontology Language: OWL
Ontologies in OWL and OWL Formal Semantics
Logic and Inference: Rules
Midterm
Query Languages
Ontology Engineering
Applications: BioInformatics
Applications: E-Commerce
Project Presentations
Project Presentations
45
Course Title-Course Code:
3D GAME PROGRAMMING - 5221329
Name of the Programme:
DEPARTMENT OF COMPUTER ENGINEERING
Semester
Teaching Methods Credits
Lecture Recite Lab. Project Homework Other Total Credit ECTS Credit
2 42 34 56 56 188 3 7.5
Language Turkish
Compulsory /
Elective Elective
Prerequisites -
Course
Contents Software and hardware architecture of computer graphics, graphics processors, 3D graphic libraries,
geometric transformations, 3D cameras, projections, graphic engines, programming the graphics processor,
effects, indexing, collision detection methods
Course
Objectives
Understanding the both software and hardware architecture of 3D graphics production in computing
platforms. Understanding the both software and mathematical background of 3D graphics generation
pipeline. Generating the 3D graphics using OpenGL. Generating various effects using graphics hardware.
Realizing the graphics engines. Analyzing the indexing and collision detection techniques.
Learning
Outcomes and
Competences
Developing a sample game using techniques discussed during the course. Developing an original effect using
graphics hardware. Understanding an advanced graphics engine core and using it in a sample Project.
Textbook and
/or References
1. Computer Graphics with OpenGL. Donald Hearn, M. Pauline Baker2. Pattern Classification, R.O. Duda,
P.E. Hart, D.G. Stork, Wiley, 2000.
Assessment
Criteria If any,mark
as (X)
Percent
(%)
Midterm Exams x 20
Quizzes - -
Homeworks x 20
Projects x 30
Term Paper - -
Laboratory Work - -
Other - -
Final Exam x 30
Instructors Lecturer Dr. Murat HACIÖMEROĞLU
Week Subject
46
1
2
3
4
5
6
7
8
9
10
11
12
13
14
General Introduction
Introduction to 3D graphics and parallel processing architecture of graphics cards.
3D graphics pipeline.
2D geometrik transformations.
3D geometric transformations.
3D camera and developing the camera class.
Projections.
Clipping algorithms.
Developing the human computer interface.
Graphics and game engines.
Shader programming
Shader programming
Level of Details
Project presentations
47
Course Title-Code:
WIRELESS NETWORK SECURITY - 5231329
Program Name:
DEPARTMENT OF COMPUTER ENGINEERING
Semester
Teaching Methods Credits
Lecture
Rec.
Lab.
Project
Homework
Other
Total Credit ECTS Credit
1-2 42 50 - - 38 - 58 188 3 7,5
Language Turkish
Compulsory /
Elective
Technical Elective
Prerequisites No
Course
Contents
Fundamentals of Wireless Networks, Wireless Network security needs, Cryptographic protocols,
Security of existing Wireless Networks, Security of emerging Wireless Networks, Secure addressing
and naming, rd, spins, LEAP+, ChanPS, HubauxBC, URSA, KarlofWagner, Wormhole attacks,
Ariadne, tinySeRSync, CapkunRCS, MolnarWagner, CapkunHJ
Course
Objectives
Teaching Fundamentals of Wireless Networks, Wireless Network security needs, Cryptographic
protocols, Security of existing Wireless Networks, Security of emerging Wireless Networks, Secure
addressing and naming, rd, spins, LEAP+, ChanPS, HubauxBC, URSA, KarlofWagner, Wormhole
attacks, Ariadne, tinySeRSync, CapkunRCS, MolnarWagner, CapkunHJ
Learning
Outcomes and
Competences
Learning Fundamentals of Wireless Networks, Wireless Network security needs, Cryptographic
protocols, Security of existing Wireless Networks, Security of emerging Wireless Networks, Secure
addressing and naming, rd, spins, LEAP+, ChanPS, HubauxBC, URSA, KarlofWagner, Wormhole
attacks, Ariadne, tinySeRSync, CapkunRCS, MolnarWagner, CapkunHJ.
Textbook and
/or
References
"Security and Cooperation in Wireless Networks", Levente Buttyan and Jean-Pierre Hubaux, , Cambridge University Press, ISBN 9780521873710 “Network Security: Private Communication in a Public World (2nd Edition)”, by Charlie Kaufman,Radia Perlman, and Mike Speciner, Prentice Hall, ISBN-10: 0130460192 "Guide to Wireless Network Security", John Vacca, Springer
Assessment
Criteria
If any, mark
as (X)
Percentage
(%)
Midterm Exams
X
30
Quizzes
-
-
Homeworks
X
30
Projects
-
-
Term Paper
-
-
Laboratory Work
-
-
Other
-
-
Final Exam
X
40
Instructors
Lecturer. Dr. Muhammet ÜNAL, [email protected]
Week Subject
48
1
2 3
4
5
6
7
8
9
10
11
12
13
14
Fundamentals of Wireless Networks
Wireless Network security needs
Cryptographic protocols
Security of existing Wireless Networks
Security of emerging Wireless Networks
Secure addressing and naming
Establishing Security Associations (rd, spins, LEAP+)
Establishing Security Associations (ChanPS, HubauxBC, URSA)
Establishing Security Associations (URSA)
Secure routing (KarlofWagner)
Secure routing (Wormhole attacks)
Secure routing (Ariadne)
Secure Services and Applications (tinySeRSync, CapkunRCS)
Secrecy and Privacy (MolnarWagner, CapkunHJ)
49
Course Code-Title 5241329 Machine Learning
Credits (3-0) 3
Prerequisite(s)
Instructor Dr. Oktay YILDIZ
Email [email protected]
Web http://w3.gazi.edu.tr/~oyildiz/
Description
Learning concepts, Decision trees, Genetic algorithms, Bayesian learning, Artificial
neural networks, Support Vector Machine, Comparison of learning algorithms,
Unsupervised learning.
Textbook and
References Machine Learning, Tom Mitchell
Course objectives To understand the basic machine learning techniques and algorithms and to apply
them to real-world problems.
Learning outcomes
To choose the most appropriate machine learning method for a given problem and dataset.
To write computer programs implementing these methods.
To evaluate the results obtained.
Grading Criteria Midterm Homework Lab Quiz Project Final exam
30 10 - - 20 40
Lectures
1. Introduction to machine learning
2. Concept Learning
3. Decision Tree
4. Genetic Algorithms
5. Genetic Algorithms
6. Project Presentations
7. Bayesian learning
8. Midterm
9. Neural networks
10. Project Presentations
11. Support Vector Machines
12. Evaluation of learning algorithms
13. Unsupervised Learning
14. Project Presentations
50
5980029 SEMINAR DEPARTMENT OF COMPUTER ENGINEERING
Semester
Teaching Methods Credits
Seminar Library Studies
Project Presentation
Other Total Credit ECTS Credit
1-2 28 80 80 188 0 7.5
Language Turkish
Compulsory / Elective
Compulsory
Prerequisites Assignment of the supervisor
Course Contents
Presentation of the thesis work
Course Objectives
To give the ability of the oral presentation and discussion
To decide on the objectives of the thesis work and the strategy
Learning Outcomes and Competences
To have the ability of the oral presentation and discussion
To have an ability of determining the objectives and the strategy of a scientific work
Textbook and /or References
All the references related to the study.
Assessment Criteria
If any,mark
as (X)
Percent (%)
Seminar X
Quizzes
Homeworks
Projects / Presentation X
Term Paper
Laboratory/ Library Work X
Other
Final Exam
Instructors The supervisor
51
5001029 MS Thesis DEPARTMENT OF COMPUTER ENGINEERING
Semester
Teaching Methods Credits
Meeting Recitation/
Lab. Other Total Credit
ECTS Credit
1-2 14 200 36 250 0 10
Language Turkish
Compulsory / Elective
Compulsory
Prerequisites Assignment of the supervisor
Course Contents
MS thesis work
Course Objectives
To improve the ability of getting the scientific information, its evaluation and interpretation by conductive scientific research
Learning Outcomes and Competences
To have the ability of getting the scientific and technological information, and engaging in life-long learning
To have the ability of evaluation and interpretation
Textbook and/or References
All the references related to the study.
Assessment Criteria
If any,mark
as (X)
Percent (%)
Midterm Exams
Quizzes
Homeworks
Projects
Term Paper
Laboratory and Library Work / Applications X
Other ( Report, presentation) X
Final Exam
Instructors The supervisor
52
80*29DD SPECIAL TOPICS in MS DEPARTMENT OF COMPUTER ENGINEERING
Semester
Teaching Methods Credits
Theory Library/Lab./ Homework
Project /
Area studies
Other Total Credit ECTS Credit
1-2 42 150 30 28 250 0 10
Language Turkish
Compulsory / Elective
Compulsory
Prerequisites Assignment of the supervisor
Course Contents
Basic concepts and applications related to the thesis work
Course Objectives
To give the general knowledge related to the thesis work
To develop the ability of analytical thinking
Learning Outcomes and Competences
To have the general knowledge
To have the ability of making plans for the research work
Textbook and /or References
All the references related to the study.
Assessment Criteria
If any,mark
as (X)
Percent (%)
Midterm Exams
Quizzes
Homeworks
Projects / presentation X
Term Paper
Laboratory / Library Work X
Other
Final Exam
Instructors The supervisor
53
6001029 PhD Thesis DEPARTMENT OF COMPUTER ENGINEERING
Semester
Teaching Methods Credits
Meeting Recitation/ Lab.
Other Total Credit ECTS Credit
1-2 14 200 36 250 0 10
Language Turkish
Compulsory / Elective
Compulsory
Prerequisites Assignment of the supervisor
Course Contents PhD thesis work
Course Objectives
To give the ability of carrying out independent research,
To give the ability of deducing conclusions scientifically
To give the ability of determining progressive steps to reach new synthesis
Learning Outcomes and Competences
To gain ability for innovations in scientific approach or to develop a new scientific method or to apply obvious method to a new field.
Textbook and /or References
All the references related to the study.
Assessment Criteria
If any,mark
as (X)
Percent (%)
Midterm Exams
Quizzes
Homeworks
Projects
Term Paper
Laboratory and Library Work / Applications X
Other ( Report, presentation) X
Final Exam
Instructors The supervisor
54
8000029 DOCTORAL QUALIFYING EXAMINATION
DEPARTMENT OF COMPUTER ENGINEERING
Semester
Teaching Methods Credits
Individual work Other Total Credit ECTS Credit
I-II 400 38 438 0 17.5
Language Turkish
Compulsory / Elective
Compulsory
Prerequisites To complete the minimum course credit
Course Contents
The written and oral exams on basic subjects and related fields of the PhD thesis work
Course Objectives
To check the qualification on basic subjects and related fields of the PhD thesis work
Learning Outcomes and Competences
To have the qualification on basic subjects and related fields of the PhD thesis work
Textbook and /or References
All the references related to the study.
Assessment Criteria
If any,mark
as (X)
Percent (%)
Midterm Exams
Quizzes
Homeworks
Projects
Term Paper
Laboratory Work
Other
Qualıfying Exam
Instructors Qualification committee
55
8500029 PROGRESS IN THESIS DEPARTMENT OF COMPUTER ENGINEERING
Semester
Teaching Methods Credits
Report, Presentation
Measurement and
evaluation Other Total Credit
ECTS Credit
I-II 40 100 48 188 0 7.5
Language Turkish
Compulsory / Elective
Compulsory
Prerequisites Passing the qualification exam
Course Contents
Developing the research work
Course Objectives
To analyse the results obtained according to the work plan of PhD studies and make the work plan for the next period and contributing to the direction of the PhD work.
Learning Outcomes and Competences
To get an ability of making work plans on the basis of research objective and evaluating the results and presentation.
Textbook and /or References
All the references related to the study.
Assessment Criteria
If any,mark
as (X)
Percent (%)
Midterm Exams
Quizzes
Homework
Projects
Term Paper
Laboratory Work
Report and presentation X
Final Exam
Instructors Thesis committee
56
90*29DD SPECIAL TOPICS in PhD DEPARTMENT OF COMPUTER ENGINEERING
Semester
Teaching Methods Credits
Theory Library/Lab./ Homework
Project /
Area studies
Other Total Credit ECTS Credit
1-2 42 150 30 28 250 0 10
Language Turkish
Compulsory / Elective
Compulsory
Prerequisites Assignment of the supervisor
Course Contents
Basic concepts and applications related to the thesis work
Course Objectives
To give the general knowledge related to the thesis work
To develop the ability of analytical thinking
Learning Outcomes and Competences
To develope the ability of analytical thinking
To get the ability of evaluation, data analysis and making written/oral presentation
Textbook and /or References
All the references related to the study.
Assessment Criteria
If any,mark
as (X)
Percent (%)
Midterm Exams
Quizzes
Homeworks
Projects / Presentation X
Term Paper
Laboratory / Library Work X
Other
Final Exam
Instructors The supervisor