|Course Code||Course Name||Theoretic - Practical - Credit|
|CMP601||Graduate Seminar||2 2 3|
|CMP603||Programming Languages Theory||3 0 3|
|CMP604||Computational Theory||3 0 3|
|CMP614||Text Mining||3 0 3|
|CMP625||Computer Networks and Open Systems||3 0 3|
|CMP634||Advanced Computer Architecture||3 0 3|
|CMP640||Software Development for Real-Time Systems||3 0 3|
|CMP641||Software Development for Parallel Computer||3 0 3|
|CMP642||Operating System Theory||3 0 3|
|CMP652||Next Generation Database Management Systems||3 0 3|
|CMP653||Database Management Systems||3 0 3|
|CMP655||Wireless Networks||3 0 3|
|CMP656||Advanced Computer and Network||3 0 3|
|CMP661||Cryptography||3 0 3|
|CMP662||Mobile Computing||3 0 3|
|CMP670||Statistical Natural Language Processing||3 0 3|
|CMP671||Probabilistic Learning||3 0 3|
|CMP672||Research Methods in Software Engineering||3 0 3|
|CMP673||Business Process Management||3 0 3|
|CMP674||Parallel Computing with GPUs||3 0 3|
|CMP681||Information Retrieval Systems||3 0 3|
|CMP682||Artificial Intelligence||3 0 3|
|CMP684||Neural Networks||3 0 3|
|CMP691||Academic Skills and Practices||0 2 1|
|CMP694||Graph Theory||3 0 3|
|CMP701||Graduate Seminar||2 2 3|
|CMP710||Parallel and Distributed Simulation Systems||3 0 3|
|CMP711||Natural Language Processing||3 0 3|
|CMP712||Machine Learning||3 0 3|
|CMP713||Data Mining||3 0 3|
|CMP715||Advanced Computer Graphics||3 0 3|
|CMP716||Evolutionary Computation||3 0 3|
|CMP717||Image Processing||3 0 3|
|CMP719||Computational Vision||3 0 3|
|CMP720||Embedded System Design||3 0 3|
|CMP722||Advanced Computer Vision||3 0 3|
|CMP723||Geographic Data Processing||3 0 3|
|CMP724||Game Technologies||3 0 3|
|CMP725||Semantic Web Programming||3 0 3|
|CMP726||Information Filtering||3 0 3|
|CMP727||Intelligent Systems and Soft Computing||3 0 3|
|CMP730||Pattern Classification Methods||3 0 3|
|CMP735||Speech Recognition||3 0 3|
|CMP741||Advanced Analysis of Algorithms I||3 0 3|
|CMP742||Advanced Analysis of Algorithms II||3 0 3|
|CMP744||Compiler Construction||3 0 3|
|CMP751||Advanced UML Modelling||3 0 3|
|CMP755||Robotics||3 0 3|
|CMP756||Swarm Systems||3 0 3|
|CMP781||Software Quality Management||3 0 3|
|CMP782||Software Metrics||3 0 3|
|CMP791||Research Methods in Computer Science||0 2 1|
|CMP793||Advanced Academic Skills and Practices||0 2 1|
Graduate students have to present prestudy of their thesis and prepare a paper about their literature research.
Conceptual study of programming language syntax, semantics and implementation, Lamda calculus and functional languages, fundamentals and language features of denotational semantics, mathematics of recursion, first order logic and declerative languages, axiomatic semantics and consistency of semantic definitions.
The theory of computability and complexity. The computability part includes Church-Turing thesis, decidability, reducibility, recursion theorem and computational learning theory. The complexity theory part includes complexity measures, complexity classes, time complexity, space complexity and intractability.
Unstructured text processing methods, Topic models and statistical models, Pattern based information extraction methods, Graph theory based text mining, Semantic Analysis, Apllication of Natural Language Processing.
Introduction to Computer Networks, Basic networking terminology, TCP layers: Transport Layer: Connectionless communication, Connection-oriented communication, Reliable communication algorithms, TCP and UDP protocols, Congestion control, Flow Control, Socket mechanism, Socket programming, RPC mechanism, Network Layer: Packet Switching, Circuit Switching, IP addresses, IP protocol, Routing tables, Routing algorithms: Link-state and Distance-vector algorithms, RIP, BGP, OSPF protocols, Hierarchical routing, NAT, ICMP protocol, Data-link Layer: Link-level (MAC) addressing, Multiple access protocols, Aloha, Ethernet, Token-ring, ARP and RARP protocols, Point-to-point protocols, PPP, Quality of service, ATM, MPLS, Local area network design, Switch and Router distinction, Proxies, Introduction to wireless networks, 802.11 protocol, Bluetooth, Wi-MAX, GSM networks, Mobile IP, MANET networks, Introduction to peer-to-peer networks, Sample peer-to-peer networks: Napster, Gnutella, Torrent, Chord.
Evolution of computer architecture and the factors influencing the design of hardware and software elements of computer systems. Topics include memory architectures, cache and virtual memory organizations, virtual memory implementation, CPU and Pipelines, RISC architectures, vector processors, multiprocessing systems.
Modular structure that provides data, type and packeting subprograms, performing processes running in parallel on real-time programming possibilites, exception handling, dependent features of data string with the provider of complete control over the string forms of involvement in an algorithmic programming language with facilities including the development of modular software and parallel process issues and structures roles. Although object-oriented algorithmic languages are not object-oriented languages,especially like modular structures, examination of aspects of object oriented programming prone.
Introduction to parallel computer architecture. Data communication on parallel computer. Program development process. Common problems that require parallel processing and solution types. Parallel programing error detection methods. Event tracking, imaging analysis methods and tools on parallel programs.
Process Synchronization: cocept of process, concurrent processes, sequencers and eventcounts, (OR, AND, NOT) synchronizations, interprocess communication; Language Mechanisms for Concurrency; Deadlocks: definition, concepts from graph theory, general model and special cases, recovery and prevention from deadlocks; Virtual Memory: stack algorithms, working sets, models of virtual memory, clock algorithms; Distributed Systems, Computer Security.
New data types. Expanded database functions: data management, object management and knowledge management. Query languages. rule definiton and rule management. Time dimensions in databases. Version concept. Multi media databases. Advanced subjects about database management system implementation techniques.
Object-relational and object oriented data models. Query processing, query optimization. Transaction processing. Associated control. Recovery system. Database system architecture. Distirbuted databases. Parallel databases. Application development and management. Advanced-level data types. Advanced topics on transaction processing. Data mining. Data warehouses.
The module surveys the emerging field of wireless networks (including mobile ad hoc networks, wireless sensor networks, vehicular networks). The course will cover a broad range of topics, including radio communication, networking protocols, energy management, mobility, and applications. Introduction Wireless Networks , Wireless Sensor Networks, Mobile Ad hoc Networks, Vehicular Networks , Networking and Routing Protocols, Cooperation in Wireless Networks , Mobility, Understanding Low-Power Wireless Networks, Security in Wireless Networks.
This course presents the principal protocols and applications that are used in the Internet today, their vulnerabilities and how they are exploited. The prevention and detection techniques in the literature are examined. Introduction to Security, Identification and Authentication, Vulnerabilities, Vulnerability Scanning, Attacks, Bot-nets, Firewalls and Traffic Filtering, Monitoring, Audit, and Intrusion Detection, Cryptography , Security in Wireless Networks, Future Issues.
Classical ciphers: Ceaser cipher, Vigenere cipher, Vernam cipher, Jefferson wheel cipher, and the Enigma machine. One-time pad. Perfect secrecy. Block ciphers: DES, AES. Encryption modes. Hash functions: MD5, SHA1. HMAC standard. Number theory. Chinese remainder theorem. Discrete Logarithms. Quadratic residues. Factoring large numbers. Diffie-Hellman Key Exchange. Pohlig-Hellman cipher, Mental Poker. Public-key cryptography: RSA, ElGamal.Digital Signatures. X509. Oblivious transfer. Blind signatures. Group signatures.Certified email. PGP.Fair exchange. Digital cash. Electronic voting.Threshold cryptography. Elliptic curves.Zero-knowledge protocols.Kerberos.
Understanding and building systems support mechanisms for mobile computing systems including mobile ad hoc and sensor networks for achieving the goal of anytime, anywhere computing in wireless mobile environments. The fundamental concepts of mobile computing are introduced. Operating systems, programming languages, and protocols for sensor networks, mobility and service management, routing in mobile ad hoc and sensor networks, mobile communication, mobile hardware and software.
Probabilistic Language Modelling, Probabilistic Context Free Grammars (PCFGs), Parsing with PCFGs, Collocations and Clustering, Part-of-Speech Tagging and Hidden Markov Models, Bayesian Language Modelling, NonParametric Bayesian Language Modelling, Word Class Induction By Distributional.
Probability Concepts, Generative Models for Discrete Data, Bayesian Statistics, Frequentist Statistics, Mixture Models, Linear Regression, Hidden Markov Models, Sampling, Graphic Models.
Course introduction and overview, Introduction to Research Methods in SE, Systematic Literature Review (SLR) and Systematic Mapping (SM) studies and SM guideline papers, Presentation of chosen SM papers, Research design and Planning, Experiments, Case Studies, Presentation of chosen Empirical SE (EMSE) papers, Presentation of students SM work in the class.
Introduction to Business Process Management, identificaiton of business processes, business process modeling and process discovery, qualitative and quantitative process analysis methods, business process automation and process intelligence.
Trends in Parallel Computing and GPU Computing, Fundamentals of Parallel Algorithms, GPU Programming Model, GPU Hardware Specifications, Parallel Computing Patterns, Optimization Techniques, Hands on experience on Sample Applications.
The theory, design, and implementation of text-based information systems. Course topics include knowledge access systems, statistical characteristics of text, representation of information needs and documents, several important retrieval models (boolean, vector space, probabilistic, inference net, language modeling), search engines, development of network agents, Turkish search engines, semantic search, ontology, XML, RDF, OWL.
Basic knowledge representation, problem solving and learning methods of artificial intelligence. Problem-solving techniques such as state-space approach, problem-reduction approach, problem model, problem representation, exhaustive search algorithms, heuristic search algorithms (A*). Game-playing. Knowledge representation and reasoning: syntax, semantics and proof theory of propositional logic, first-order predicate logic, production systems, semantic nets and frames. Knowledgebase, expert systems, inference engine. Machine learning: inductive inference, analogical inference, learning by instruction, learning from examples, conceptual clustering, explanation-based learning.
Basic components of Neural Networks, topologies, learning algorithms, classifiers. Single-layer perceptron classifiers, Advanced multi-layer information flow networks, Single-layer feedback networks, Associative memories, Supervised and unsupervised learning techniques. Neural network applications.
Definitions: research, contribution, experimentation, literature, hypothesis, axioms, theorems, proofs; Experimentation: methodology, verification, analyzing results, presentation results (charts, graphs, curves, etc.); Presentation: abstract, related work, proposing an idea, verification by experimentation/proofs, evaluating results, conclusions and thesis writing; Seminar Presentation; Conferences, symposiums, workshops; Academic degrees : bachelor of science, master of science, doctorate of philosophy; Ethical issues: plagiarism, slicing, duplication, sufficiency of contribution.
Graph description, "walk","path","circuit" etc. concepts. Various types of graph: directed graph, bipartite graph, complete bipartite graph, k. degree complete graph, connected graph, cycle graph, noncycle graph, isomorphic graph. Graph representations, Isomorphism, Sub-graphs, Graph complement, Euler cycle, Hamiltonian cycle. Graph applications: Minimum spanning tree, Maximum flow problem, Shortcut path problem, Chromatic graph. Graph theorems.
Graduate students have to present prestudy of their thesis and prepare a paper about their literature research.
Introduction to simulation systems. Basic concepts in discrete-event simulation. Time matching techniques in parallel and distributed discrete-event simulation systems. Computer network, data sharing, time management and event sequencing in distributed virtual environments.
Course topics include brief history and overview of natural language processing, morphological processing, part of speech tagging, context-free grammars, top-down/bottom-up parsing, chart-parsing such as Earley parsing, unification based grammars, word-sense disambiguation, semantic analysis, and some natural language processing applications. Some of the covered natural language applications are information extraction, machine translation, dialogue systems, and text summarization.
An overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as concept learning, decision tree learning, artificial neural networks, Bayesian learning, instance-based learning, genetic algorithms, reinforcement learning. The course gives the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work.
Introduction to data mining techniques, including data mining pipeline, data preprocessing and cleaning, data reduction, data mining primitives, cluster analysis, clustering, association rule mining, series analysis and sequence mining, graph mining, web mining, data visualization, and data warehousing. Detailed applications from a wide variety of domains.
Sampling, shading, and display aspects of computer graphics. Operating principles of graphic viewers and hardware, high-performance architectures for graphics, interactive graphics hardware and software. Point-drawing techniques, line-drawing techniques, two-dimensional transformations, window techniques, texture generation and rendering, volume rendering, strategies for anti-aliasing and photo-realism. Introduction to three-dimensional graphic and transformation techniques, human vision and color science as they relate to computer displays.
The main areas covered are the systems inspired by natural evolutionary systems and the principal algorithms and applications derived from those natural systems. Biological evolution, Search spaces, fitness landscapes, fitness functions, Encodings and representations, Genetic Algorithms: crossover, mutation, selection, Genetic Programming, Grammatical Evolution, Schema theorem and building block hypothesis, Convergence, Co-evolution, niching, Multi-objective EAs, Applications.
Aim is to cover topics such as image acquisition and display, properties of the human visual system, color representations, image modeling and representation, sampling and quantization, point operations, image enhancement and restoration, image denoising, image deblurring, image impainting, image segmentation. Linear image filtering and correlation, transforms and subband decompositions, nonlinear filtering, contrast and color enhancement, dithering.
Image formation. Image processing. Lightness and Color. Feature detection and matching. Segmentation. Feature-based alignment. Structure from motion. Motion estimation. Computational photography. Stereo correspondence. Shape and appearance modeling. Visual recognition.
Topics include: microcontroller architecture, embedded software design, interrupt synchronization, timing generation and measurement, serial and parallel I/O interfacing, analog interfacing, digital and analog peripheral circuit design. Dedicated to system design based on the basic components such as: microprocessors, memory units, I/O interfaces, utility operators, I/O drivers as sensors, electromechanical guidance tools, microcontrollers.
Advanced research topics in computer vision including low-level vision, object recognition, face detection and recognition, image and video retrieval, human detection, human motion analysis, camera geometry and calibration, geometrical and stereo vision, 3D scene reconstruction, visual tracking, human motion capturing and recognition, with the applications to advanced video processing and vision-based modeling and interaction.
Defining GIS and Introduction to Spatial Data File Formats, Projections and Coordinate Systems, Tabular Data Design and Functions, Data sources and data collection, The Raster Data File Format and Raster Analysis, Metadata, Georeferencing, Geocoding, Interpolation and Surface Modeling.
Introduction to technologies used to develop the game and graphics applications, Examination of software development tools that develop 3D computer games and graphics applications, Computer game's drawing and simulation problems analysis.
Semantic web technologies, which will allow data to be shared and reused across application, enterprise, and community boundaries. Topics include modeling real world, discovering information, sharing semantics, rules, semantic web patterns. Examples demonstrate how to use the semantic web to solve practical, real-world problems.
Conceptual framework for the design of information filtering systems. User modeling. Information filtering taxonomies and performance evaluation. Mathematical foundations. Linguistic Essentials. Corpus-based work. Text categorization. Topic detection and tracking. Probabilistic and information retrieval approaches to topic detection and tracking.
Introduction to Intelligent Systems, Rule-Based Expert Systems,Introduction to Expert Systems Programming, Uncertainty Management in Rule-Based Expert Systems, Fuzzy Expert Systems, Frame-Based Expert Systems, Artificial Neural Networks, Hybrid Intelligent Systems, Discussion on data set properties effecting methods, Introduction to Sampling strategies.
General definitions and mathematical foundations of Bayes Decision Theory, Maximum Likelihood, Nearest Neighbor Classification, Linear Discrimination Functions, Multilayer Neural Networks, Unsupervised Learning and Clustering.
General definitions, speech recognition system architecture, language structure, statistics and probabilty, pattern recognition, digital signal processing, feature vectors, speech coding, Hidden Markov Models (HMM), acoustic models, language models, search methods in speech recognition, wide reportery speech recognition systems.
Basic mathematical and statistical concepts. Recursive relationships: homogeneous and non- homogeneous recursive relationships. Recursive relation solutions: substitution method, iteration method, master method, proof of master method. General algorithm design techniques (divide and conquer, trackback, dynamic programming). Some sort, graph, coputational geometri algorithm analysis: "bottom-up heap sort", "Dijkstra Algorithm", "Spanning Tree (Kruskal and Prim algorithms)", "tarjan algorithms)", "closest pair of points, "convex frame". Euclidean algorithm analysis, Unification-Discovery process analysis. What is NP?, Original NP problems.
Installment analysis. Advanced data structures: B trees, spanning trees, binomial heaps, fibonacci heaps . String matches: Knuth-Morris-Pratt, rabin-Karp and Boyer-Moore algorithms. Strassen's matrix multiply algorithm, Detailed analysis of binary search, optimal heap sort, longest increasing sub-series, probabilistic and approach algorithms. Maximum flow problem.
Description of compiler. Compilation stages. Compiler Creation Tools. Word Analyzer; specification of regular expressions and syntactic units, recognition of syntactic units, finite state automata, word parser generators. Syntactic Analysis; context-free grammars, top-down and bottom-up parsing, priority operator decomposition, LR parsers, parser generators. Conversion Syntax-Driven; S-attribute and L- attribute descriptions, top-down and bottom-up converters. Type controller. Run-Time Environments, Code generation. Code Improvement.
Introduction to Information Systems modeling: Business process modeling, BPMN and BPEL, Conceptual modeling, Unifed Modeling Langauge (UML), Static diagrams, Dynamic diagrams, Recommended usage of UML diagrams in software development, Model-Driven Architecture (MDA), Meta Object Facility (MOF),Domain Specific Languages (DSLs), Model-Driven Software Development (MDSD), The concepts of model, meta-model and transformation, Object Constraint Language (OCL)
The goal of robotics is to build machines with human-like dexterity and/or intelligence and which function with minimal human intervention. This course presents the principal robotics architecture and applications: introduction to robotics, theory of robotics control, kinematic and inverse kinematics, actuators, robot controls and environments, mobile robots, localizations, sweepline algorithms, voronoi diagrams, convex hulls and linear programming, duality, epsilon-nets, dynamics and model-based control.
Quality Management Basics, Total Quality and quality management, The scope of software quality management, Software quality assurance, verification, and validation, Quality management within software development life-cycle, Software Quality Assurance, Gözden geçirme ve türleri, Product and process audits, Software quality assurance process and its outputs, Verification and Validation, The concepts of verification and validation, Verification methods, Validation methods, V Model and test levels (unit, integration, functional, acceptance),Quality Management System, The principles of Quality Management System, Process management, ISO 9001:2008 standard and its application to software eng., Quality Management System infrastructure and features.
Measurement Basics, The need for measurement, Measurement theory (scales, validation, and meaningfulness), Goal-question-metric method, Data collection and analysis principles, Software Measures, A classification of software measures, Product measures: Size, structure, quality, Process measures: Maturity, management, development, Resource measures: Personnel, software, hardware, Software Measurement Process, Measurement process models, ISO/IEC 15939: Software Measurement Process, Practical Software Measurement, Capability Maturity Model Integrated (CMMI) and measurement and analysis, Measurement Programs and Infrastructure, Measurement infrastructure requirements, Best practices for the success of measurement programs, Measurement for high maturity (statistical process control, 6-sigma)
Providing incoming graduate with basic research and working skills, facilitating a smooth transition to graduate studies and research, the course shows multiple elements including time management, writing and presentation skills, and general considerations for experiment design and planning. The students will find the answers of PhD related questions such as the following questions. What is expected from a PhD research? How to select a PhD topic? What kind of preliminary studies should be done for a PhD research? How to prepare a PhD proposal? How to perform a scientific test and how to perform the success of its results? In a specific research area of Computer Science, reading articles, discussing their contents, and preparing their summaries. Making a literature survey in a specific research area. Writing articles.
The objective of this course is to introduce students to major concepts of a graduate study and the answers to the questions like "How a graduate research should be performed" and "What are the important issues that the students should be aware during a graduate study". The students will also learn the etchical issues that they should obey during their graduate studies, gradute thesis writing and writings of scientific articles.