Bilgisayar Mühendisliği Yüksek Lisans programı, İngilizce yürütülen, tezli ve tezsiz seçenekleri olan bir programdır. Toplam 30 kredilik 10 ders ile proje çalışmasından oluşan tezsiz program iki yarıyılda, toplam 21 kredilik 7 ders ile seminer ve tez çalışmasından oluşan tezli program ise iki yarıyıl ve bir yaz dönemi sonunda tamamlanabilir.
PROGRAMIN AMAÇLARI VE HEDEF KİTLESİ
Bilgisayar Mühendisliği Yüksek Lisans programı, yaratıcı iş modellerini geliştirmeyi hedefleyen teknolojileri kullanan, bunları yerel çözümlere dönüştürebilen bir bilgi birikimini öğrencilerine kazandırmayı hedeflemektedir. Büyük veri tabanlarının sorgulanmasına yönelik veri madenciliği tekniklerinin kullanımı, bir bilişim projesinin analizi, planlanması ve yönetimi konularında uzmanlaşma ile işletmelerin etkin ve verimli çalışmasına katkıda bulunarak kaynakların sağlıklı kullanımının gerçekleştirilmesi amaçlanmaktadır.
PROGRAM YAPISI
Bilgisayar Mühendisliği Yüksek Lisans Programında dersler dört uzmanlık alanında yoğunlaşmıştır:
Bilgisayar Bilimleri Uzmanlık Alanı (BB): Temel Bilgisayar Mühendisliği programına ilgi duyan, imge işleme, ağ güvenliği ve şifreleme gibi uygulamalarda yetkinlik kazanmak isteyenleri hedef alan bir modüldür.
Yapay Zeka Uzmanlık Alanı (YZ): Veri modelleme ve analiz yöntemlerinin kullanıldığı endüstriyel sektörde, finans, sigorta, Telekom ve analiz yöntemleri ve uygulamalarını öğrenmek isteyenleri hedefleyen bir modüldür.
Kurumsal Yazılım Sistemleri Uzmanlık Alanı (KYS): Günümüzde yazılım geliştirme süreçleri yerini yazılım projelerinin yönetimine bırakmıştır. Bu modül sigorta yazılımları, hastane otomasyon sistemleri gibi kurumsal yazılım projelerinin planlanması, yürütülmesi ve gerçekleştirilmesinde farklılık yaratacak bir deneyim kazandırmayı hedeflemektedir.
Gömülü Video Sistemleri Uzmanlık Alanı (YBS): Bu alanla ilgili bilgi Gömülü Video SistemleriProgramı başlığı altında yer almaktadır.
Bilgisayar Mühendisliği yüksek lisans programında yer alan dersler ile bu derslerin her uzmanlık alanı için zorunlu mu (Z), seçmeli mi (S) olduğu ders listesinde gösterilmiştir. Öğrenciler tüm derslerini belirledikleri bir uzmanlık alanına ait dersler arasından seçmek zorundadır.
Ders Listesi
Required Courses
Master Thesis
Seminar
Project
Master Thesis
Seminar
Restricted Elective Courses
Computer Arithmetic
Data Mining I
Data Mining II
Introduction to Network Security and Cryptography
Advanced Cryptography
Advanced Computer Networks andMobile Communications I
Advanced Computer Networks andMobile Communications II
Multimedia Communications and Networking
Image and Video Processing
3-D Computer Graphics
Bioinformatics I
Bioinformatics II
Artificial Neural Networks I
Artificial Neural Networks II
Formal Languages & Automata Theory
Expert and Knowledge Based Systems
Algorithm Analysis
Embedded Systems
HDL-Based Digital Design Project
Digital Design Automation
Computer Vision and Pattern Recognition
Special Topics I
Special Topics II
Advanced System Analysis and Design I
Advanced System Analysis and Design II
Software Quality and Risk Management
Specification and Design
Distributed Database Systems I
Software Project Management
Extensible Markup Language (XML)
Distributed Database Systems II
Web Services
Introduction to IT Services Management
Information Security Management
VLSI Test and Verification
ASIC / SOC Design
Real-Time Signal Processing
Image Processing
Image and Video Compression
Video Processing
Mathematical Tools for Video Processing
Applied Linear Algebra
Numerical Methods
Software Engineering Mathematics
Ders Tanımları
Computer ArithmeticComputer arithmetic algorithms are at the heart of many digital ICs in the video market as well as communications and processor markets. A thorough grasp of these algorithms is needed in order to implement fast and small chips. Topics include 2's complement fixed point representation, basic addition and subtraction, fast adders, prefix graphs, priority encoders, carry-save trees, barrel shifters, MAC operation, division schemes, LUT based computation, floating-point numbers and operations.
Data Mining I
Introduction to data mining, data warehouse and OLAP technology for very large databases. Factor analysis for feature extraction. Considering classification algorithms: CART, ID3, neural networks, naive Bayes. Handling cluster analysis with nearest neighbor, expectation maximization, partitioning algorithms, hierarchical cluster analysis. Comparing association rules in large databases.
Data Mining II
Syntax, semantics and structure in HTML, text documents and data, the computational aspects of information extraction (IE) and integration from unstructured and semi-structured sources, regular expressions, regular tree expressions, XPath, XSLT, XQuery and hidden Markov model (HMM), horn rules, description logic, frame logic, topic maps, inductive logic programming, Meta-Data, ontologies, XML, RDF, DAML+OIL, the enabling tools, techniques and languages for semantic Web mining, Web Agents and Web Crawlers, mining ontologies from the Web, ontologies to build focused Web crawlers, domain-specific semantic search engines to improve Web searching, applications in E-Commerce and bioinformatics, how to do research in semantic web mining
Introduction to Network Security and Cryptography
Introduction into the field of cryptography and network security. Data and network security, different attacks on cryptographic systems, concepts of public and private key cryptography. Secret key schemes, DES and IDEA. The public key schemes RSA and EIGamal, and systems based on elliptic curves. Signature algorithms, hash functions, key distribution schemas.
Advanced Cryptography
Authentication applications; support application-level authentication and digital signatures. Widely used services Kerberos, X.509 directory authentication service. Electronic mail security issues. Pretty Good Privacy (PGP), S/MIME schemes. IP security (IPSEC) concept, IP security architecture, authentication and key management. Web security and standardized schemes SSL/TLS and SET. Intrusion prevention mechanisms; IDS (intrusion detection system), firewalls, NFAT (network forensics analysis tools).
Advanced Comp. Networks & Mobile Communications I
Introduction to networking, virtual private networking (VPN) theory and practice. VPN theoretical sessions and VPN lab applications, Current technologies and applications in industry, bandwidth utilization technologies in WAN networking wireless structure.
Advanced Comp. Networks & Mobile Communications II
Mobile and wireless applications, voice technologies like voice over ATM (VoATM) voice over IP (VoIP) and voice over frame relay (VoFR). Theoretical sessions and practical applications on Cisco routers. Other applications in industry (e-business servers carrier infrastructures) conceptual approach to e-business applications and other up-to-date application areas.
Image and Video Processing
A top-down analysis of video processing applications, algorithms, tools, and fundamentals. Applications include digital TV, computer games, cinema special effects, 3D TV, medical imaging, and forensics. Algorithms include motion estimation, filtering and restoration, deinterlacing and enhancement, interpolation and super resolution, stereo and 3D video processing, coding, and compression standards.
3-D Computer Graphics
Introduction to computer graphics, where computer generated pictures are used, graphics display devices, overview of graphics systems; getting started: drawing figures, device independent programming and OpenGL, anatomy of an OpenGL application, use of OpenGL in C++, basic 2-D shapes, representation of objects on the computer screen; computer graphics elements: drawing shapes, graphics output primitives, attributes of graphics primitives, geometric transformations, 2D viewing, clipping, three-dimensional viewing, OpenGL 3D viewing and projections, introduction to illumination models and shading in OpenGL, GUI design for graphics applications, introduction to computer animation and game programming.
Bioinformatics I
Sequence alignment, database searching, RNA structure prediction, microarray sequence analysis, gene prediction, repeat detection, and protein folding prediction, analysis of the algorithms behind each of these algorithms, dynamic programming, hidden Markov models, finite state automata, grammars, Karlin-Altschul statistics, and Bayesian statistics.
Bioinformatics II
Analyze and evaluate biological datasets to determine which data are important for model construction. Apply appropriate mathematical techniques to systems model building. Evaluate the predictive power of the computational and mathematical models. Use the models to suggest new experiments.
Artficial Neural Networks I
Introduction to neural networks, artificial neural networks, single layer perception, Hebbian learning, decreasing slope learning, general delta rule, learning in multi layer perceptions, feedback, learning with momentum, composite slope learning, prejudice and variety, radial basis perception applications, radial basis function networks, introduction to self organizing systems.
Neural Networks II
Dynamic neural networks and their applications to control and chaos prediction. Neuro fuzzy systems; cooperative neuro-fuzzy systems, neural networks for determining membership functions, Adeli-Hung algorithm, learning fuzzy rules using neural nets, identifying weighted fuzzy rules using neural nets. Evolutionary computing; genetic programming and algorithms.
Formal Languages & Automata Theory
Introducing formal languages and automata. Languages: using generators (e.g., grammars/regular expressions) and using recognizers (e.g., finite state machines). Along with presenting the fundamentals, this course will develop and examine relationships among the various specification methods for the regular languages and the context-free languages, in detail.
Expert and Knowledge Based Systems
Expert systems have developed as an outgrowth of research in artificial intelligence. They contain knowledge gleaned from human experts and can perform some tasks as well as and sometimes better than their human counterparts. Fuzzy sets provide a natural basis for employing uncertainty in expert systems. This course covers fuzzy sets theory and fuzzy logic, fuzzy set applications to decision making and process control, expert systems theory and architecture, and expert systems applications.
Analysis of Algorithms
Rigorous analysis of the time and space requirements of important algorithms, including worst case, average case, and amortized analysis. Techniques include order-notation, recurrence relations, etc. Analysis of the key data structures: trees, hash tables, balanced tree schemes, priority queues, Fibonacci and binomial heaps. Algorithmic paradigms such as divide and conquer, dynamic programming. Exploring selected advanced algorithms.
Embedded Systems
This course is a hands-on course that requires software work as well as board-level work where the student connects multiple building blocks to each other. This course sits at the intersection of fields such as microprocessors, digital design, operating systems, assembly programming, software design, and industrial automation.
Seminar
The purpose of this seminar is to equip the student enrolled in a program with a thesis with the necessary background for preparing a thesis. Although not compulsory, it is expected that the student prepares a pre-research document on her/his thesis subject and make a presentation at the end of the term.
Master Thesis
The Master Thesis is a study that students enrolled in a program with a thesis have to carry out under the leadership of an advisor on a subject related to the program followed. The thesis has to be prepared in line with academic ethic rules, presented to and approved by a thesis committee. The student has to register to this course for at least two terms.
Project