Program tanımları
DERS PROGRAMI
1.YIL / 1. YARIYIL
Seçmeli I (Th 3 + Pr 0) 3
Seçmeli II (Th 3 + Pr 0) 3
Seçmeli III (Th 3 + Pr 0) 3
1.YIL / 2. YARIYIL
Seçmeli IV (Th 3 + Pr 0) 3
Seçmeli V (Th 3 + Pr 0) 3
Seçmeli VI (Th 3 + Pr 0) 3
2.YIL / 1. YARIYIL
Seçmeli VII (Th 3 + Pr 0) 3
Yüksek Lisans Tezi (Th 0 + Pr 1) 0
Seminer (Th 0 + Pr 2) 0
Uzmanlık Alan Araştırmaları (Th 4 + Pr 0) 0
Uzmanlık Alan Çalışmaları (Th 4 + Pr 0) 0
2.YIL / 2. YARIYIL
Yüksek Lisans Tezi (Th 0 + Pr 1) 0
Uzmanlık Alan Araştırmaları (Th 4 + Pr 0) 0
Uzmanlık Alan Çalışmaları (Th 4 + Pr 0) 0
SEÇMELİ DERSLER
İstatistiksel Veri İşleme Giriş (Th 3 + Pr 0) 3
Sistem Modelleme ve Bilgisayarlı Benzetim (Th 3 + Pr 0) 3
Artificial Intelligence (Th 3 + Pr 0) 3
Kriptografi (Th 3 + Pr 0) 3
C 4I ve Bilgi Savaşları (Th 3 + Pr 0) 3
İleri Ağ Güvenliği (Th 3 + Pr 0) 3
Nüfus Tespiti (Th 3 + Pr 0) 3
İşlemsel Sayı Teorisi (Th 3 + Pr 0) 3
Paralel Algoritmalar (Th 3 + Pr 0) 3
İleri İşletim Sistemleri (Th 3 + Pr 0) 3
İleri Algoritmaların Tasarım ve Analizi (Th 3 + Pr 0) 3
Gerçek-zamanlı ve Gömülü Sistem Tasarımı (Th 3 + Pr 0) 3
Tıp ve Biyolojide Bilgisayar Uygulamaları (Th 3 + Pr 0) 3
Bilgisayar Bilimlerinde Çeşitli Konular (Th 3 + Pr 0) 3
ZORUNLU 8XX - 9XX DERSLERİ
AUzmanlık Alan Araştırmaları (Th 4 + Pr 0) 0
Uzmanlık Alan Araştırmaları (Th 4 + Pr 0) 0
Uzmanlık Alan Araştırmaları (Th 4 + Pr 0) 0
Uzmanlık Alan Araştırmaları (Th 4 + Pr 0) 0
Uzmanlık Alan Araştırmaları (Th 4 + Pr 0) 0
Uzmanlık Alan Araştırmaları (Th 4 + Pr 0) 0
Uzmanlık Alan Çalışmaları (Th 4 + Pr 0) 0
Uzmanlık Alan Çalışmaları (Th 4 + Pr 0) 0
Uzmanlık Alan Çalışmaları (Th 4 + Pr 0) 0
Uzmanlık Alan Çalışmaları (Th 4 + Pr 0) 0
Uzmanlık Alan Çalışmaları
Uzmanlık Alan Çalışmaları
DERS İÇERİKLERİ
Introduction to Statistical Data Processing
Introduction: Population and variants, construction of tables and graphs, samples and population
Description of sample data: Measures of the centre of a set of observations and the measure of variability
Probability: Sample space, events, counting methods, relative frequencies, probability, conditional probability
Random variables: Description of a random variable, expectation, discrete and continuous random variables
Discrete random variables: Bernoulli trials, discrete uniform, binomial, geometric, negative binomial, poisson distributions
Continuous random variables: Continuous uniform, normal, gamma, exponential, lognormal, weibull distributions
Statistical estimation: Estimation, point and interval estimates, estimation of population mean, difference between two means, estimation of p
Testing hypotheses: Basic concepts, large sample tests, distribution of t and c 2, small sample tests for mean and variances
Contingency tables: c2 tests for goodness of fit and independence
Correlation and Regression: Relations between variables, simple regression model and estimation of parameters, Analysis of variance, Multiple Linear Regression, Introduction to Non-Linear Models, correlation
Design and Analysis of Experiments: Introduction to the concepts of experimentation, simple designs, analysis of variance
Course Title: System Modelling and Computer Simulation
Introduction : Introduction to simulation and Monte Carlo methods, systems, models, review of basic statistics and goodness of fit testing, selecting input probabability distributions for system simulation. Random Numbers: R andom numbers, techniques and methos of generation of random numbers. Random Variables: Random variable generation, inverse transform, convolution, acceptance-rejection techniques. Discrete System Simulation: Discrete system simulation, time advance methods. Programming Languages: Evaluation of programming languages for discrete system simulation.
Cryptography
Definitions, History, Asymmetrical Cryptosystems, Cryptographic Protocols, Cryptographic Applications: Secrecy and Privacy, User Authentications, Integrity Check; Hash Functions, Digital Signatures, Design of Cryptosystems, Mutual Identification and Authentication Protocol Design, Cryptographic Access Control for Information Systems, Elliptic Curve Cryptosystems.
C 4I and Information Warfare
Concepts of Information in Warfare: Information model of Warfare, Current State of Information Warfare. Role of Technology in Information Warfare: Knowledge Creation and Discovery Process, Integrating Information Technologies. Information Warfare Policy and Strategy: Operational Model of Information Warfare, Implementing Information Warfare Policy and Strategy. Elements of Information Operations: Defensive Operations, Offensive Operations. Offensive Information Operations: Attack Matrix for C 4I and Information Warfare. Defensive Information Operations: Survivable Information Structures, Defense Tools and Services. Technologies of Information Warfare: Collection, Processing, Dissemination and Presentation Technologies of Information Warfare.
Advanced Network Security
Conventional Encryption: Symmetric encryption algorithms; Public Key Cryptography: Asymmetric encryption algorithms; Authentication Applications: Kerberos and X.509; Electronic Mail Security: PGP and S/MIME; IP Security: IPSec protocol and its application; Web Security: SSL, TLS and SET; Network Management Security: Secure SNMP
Intrusion Detection
Firewalls: Eliminating attacks at the enterence of a network. Limitations of Firewalls: Attacks that firewalls is not able to prevent. Computer Misuse Techniques: Classification of misuse techniques and examples. Intrusion Detection Model: Generic model used in intrusion detection. Misuse Detection: Signature based intrusion detection. Anomaly Detection: Intrusion detection through abnormal behavior. Intrusion Detection Systems: Architecture and functional components of IDS.
Computational Number Theory
Fundamentals, Algorithms for Congruences, Equations, and Powers, Euler’s Φ Function and Coding, Second Degree Congruences, Prime Numbers, Quadratic Residues, Continued Fractions, Algorithms for Primality Testing, Finding Large Primes, Elliptic Curves, Factoring Algorithms, Algorithms for Exponential Methods of Factoring Integers, Subexponential Factoring Algorithms, Computing Discrete Logarithms.
Parallel Algorithms
Parallel Programming Models; parallel architectures; distributed and shared memory architectures, communication operations on ring, mesh and hypercube, parallel programming languages and environments; C*, Occam, PVM, MPI, iPSC Hypercube, parallel matrix algorithms and linear equations, parallel graph algorithms, scheduling.
Advanced Operating Systems
Operating systems review, process synchronisation, distributed system communication, synchronisation in distributed systems, distributed algorithms, static and dynamic scheduling in distributed systems, group communication, fault tolerance, distributed real-time systems.
Design and Analysis of Advanced Algorithms
Mathematical background: asymptotic notation; recurrences: substitution,iteration and master methods; Sorting: Heaps and heapsort; quicksort; sorting in linear time; Dynamic programming; Greedy Algorithms; Elemantary graph algorithms: Breadth First Search, Depth First Search; Minimum spanning trees: Kruskal and Prim algorithms; Single source shortest paths; All-pairs shortest paths; Maximum flow; Matrix operations: Strasen algorithm; solving linear equations; sparse matrices; Paralel algorithms .
Real-time and Embedded System Design
Architecture of real-time systems, real-time kernels, scheduling and resource management, real-time languages; real-time comunication protocols, distributed real-time systems, fault tolerance, reliability in distributed real-time systems.
Computer Applications in Medicine and Biology
The analysis and design of Hospital Information Systems. The mathematical, physical and Physiological basis for algorithms used in medicine, imaging and biological modeling. Topics in computer modeling of organs will be chosen from the brain, heart, nervous system, sense organs, etc.
Topics in Computer Science
Topics in advanced areas will be discussed in seminar form. Contents may vary.
Special Studies
The precise content of this course consists for each student of a given aspect of the topic of his or her thesis at the junction of student interest and lecturer’s specialisation. The course guides in the researching of a specific topic, a relevant theoretical problem or in literature survey. Content is further determined by stage at which the student’s thesis study is: student may write a Review of the Literature, an essay, article, thesis chapter, or prepare oral presentations for delivery to lecturer.
