Course Content

An orientation course to provide counsel to the students about the Department and Computer Engineering in general. An introduction to ethical and legal issues related to computer programs. An introduction to the faculty and their activities. Visit to several Computer Centers in and outside the University.

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Introduction to fundamentals of computer systems, including computer organization, operating systems, language processors and user interfaces. Introduction to algorithms and programming. Reasoning informally about the correctness and efficiency of programs. A functional programming language will be used for practical work.

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Advanced programming with C: storage and control structures, recursion and programming with dynamic data structures. Homeworks are required to run on Unix environment.

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Basic introduction to computers and programming. Flowcharting, Fortran control statements, input-output statements, loops, arrays and subprograms. Labs covering an introduction to DOS, word processors, database overview and other general topics. (Offered to non-CENG students only).

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Fortran: Algorithmic approaches and flowcharting, elements of Fortran control statements, loops, arrays, formatted I/O, subprograms, character expressions, File I/O, advanced statements, external lib. handling. Literacy: Computers, their components, terminology, peripherals, operating system concept, spectrum of softwares, some software tools and packages. (Offered to non-CENG students only).

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Classification of data structures, space and time considerations. Linked lists, stacks and queues,priority queues . Array and pointer based implementations.Heaps and Tree structures, binary search trees,balanced AVL and red-black trees. Fundamentals of sorting and searching. Hash-tables and collision handling.Representation of graphs and introduction to graph algorithms. Prerequisite: CENG 140

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Introduction to probability. Discrete and continuous random variables and their distributions. Simulations of random variables. Descriptive statistics. Statistical inference. Regression. Monte Carlo methods. Stochastic processes. Queuing systems.
Prerequisite: MATH 120

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Fundamentals of logic, set theory, relations, functions, induction, graph theory, trees, introduction to algebraic structures, lattices.

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C Programming; constants, variables, expressions, statementes; selective structures; repetitive structures and arrays; functions and recursive programming; pointers, strings and multi-dimensional arrays; structures and unions; dynamic memory management&linked-lists; bit-wise operations; file processing. (Offered to non-CENG students only)

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Introduction. Constants, variables, expressions, statements. Selective structures. Repetitive structures and arrays. Functions. Pointers. Multi-dimensional arrays. (Offerd to non-5710 students only.)

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

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Basic computer organization; binary data representation; introduction to Python; basic and container data types; variables, expressions, statements; repetitive programming; algorithmic thinking; functions; working with files; exception handling and debugging; scientific programming.

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Evolution of programming languages. Overview of language translation, virtual machines, and run-time environments. Names, bindings and scopes. Values, expressions and types. Type compatibility and type checking. Storage, variables, and commands. Procedural abstraction. Generic units. Overview of functional programming paradigm. Overview of object-oriented programming paradigm: encapsulation, classes and objects, inheritance, polymorphism, dynamic binding.

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Introduction to strings, languages and grammars. Concept of abstract machines and language acceptance. Deterministic and non-deterministic finite state machines. Regular expressions. Machines with pushdown tape. Turing Machines and recursive functions.

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A minimum of four weeks (20 working days) of training and practice in an ICT (Information and Communication Technologies)-intensive organization, pertaining to the system lifecycle processes of the organization. Students are expected to be involved in the research, development, evolution and operation activities for the software/data-intensive systems that are under the responsibility of the organization` s ICT or R&D (Research&Development) departments/teams. Specific guidance about the course conduct are found in the department` s summer practice manual.

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Linked lists, stacks, queues, recursion, trees, sorting, searching, introduction to graphs, space and time consideration. (Offered to non-CENG students only).

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Introduction to DBMSs, Relational Databases, Relational Algebra, SQL, Entity Relationship Model. Theory of Database Design, Physical Database Design, Example DBMS. (Offered to non-5710 students only).

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Object Oriented Programming concepts ,classes and objects, using constructors and set-get methods, method overloading ,inheritance and polymorphism, java language elements.

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Introduction to Algorithms, Space and Time Considerations, Python built-in types, Linked lists, Stacks, Queues, Recursion. Trees, Sorting, Searching, Implementations in Python, Introduction to graphs. (Offered to non-CENG students only)

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Selected computer algorithms: sorting, searching, string processing and graph algorithms. Algorithm design and analysis techniques. Time and computational complexities of algorithms. Introduction to NP-completeness, parallelization of algorithms, linear and dynamic programming.

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Advanced algorithmic problems in graph theory, combinatorics, and artificial intelligence. Creative approaches to algorithm design. Efficient implementation of algorithms.

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Microprogramming and microprogrammed control. Input and output devices, I/O interfaces. Memory hierarchy and memory management. Fast arithmetic: fast multipliers and floating point operations. Reduced instruction set computer. Computer communications.

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Introduction to Operating Systems. Memory Management. Process Management. Concurrent Processes. Deadlocks. Processor Management. I/O and Device Management. File Management and File Systems. Introduction to Distributed Operating Systems. Synchronization in Distributed Systems. Distributed File Systems. Overview of contemporary OS technology.

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Assembly language and controller architecture. Peripheral interfaces: A/D and D/A conversion, parallel and serial ports, interrupts and timers/counters. I/O bus architectures. Sensors and actuators. Design and analysis techniques. Real time operating systems.
Prerequisite: CENG 232.

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Overview of the base language of a Rapid Application Development (RAD) tool; object definitions, methods, properties and inheritance. Form design using visual components Application development using the libraries of an industry standard RAD tool.

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Software Project Management: metrics, estimation, planning. Software requirement analysis techniques. Software design techniques. Software implementation. Software quality assurance. Software testing. Software maintenance. Review of CASE technology.

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Introduction to DBMSs. Relational databases. Entity-Relationship data model. Relational algebra. Relational calculus. SQL. Sequential files. Unordered sequential files. Ordered sequential files. External sorting. B+tree index. Hashing. Dynamic hashing. Basics of query evaluation.

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Review of relational model of data,relational algebra, and SQL. Semi-structured data models. Relational database design theory. Query evaluation and optimization. Performance tuning. Transaction management, concurrency and crash recovery. Client-server architectures. NoSQL databases.

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Accuracy in numerical analysis. The sources and propagations of errors. Solution of non-linear equations. Interpolating polynomials. Solution of linear algebraic equations. Least squares curve fitting. Numerical integration.

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Linear systems in discrete and continuous time. State vectors. Eigenvalues/eigenvectors and diagonalization. System behavior. Nonlinear systems. Fixed points. Stability. Linearization. Lyapunov,s method. Periodicity and chaos in discrete time. Fractals. Complex dynamical systems.

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Linear time invariant systems; Frequency domain; Periodic and finite signals; Frequency response; Fourier series and transforms; Filtering; Finite impulse response filters; Sampling and reconstruction.
Prerequisites: MATH 219 and MATH 260.

Course Content

A minimum of four weeks (20 working days) of training and practice in an ICT (Information and Communication Technologies)-intensive organization, pertaining to the system lifecycle processes of the organization. Students are expected to be involved in the research, development, evolution and operation activities for the software/data-intensive systems that are under the responsibility of the organization`s ICT or R&D (Research&Development) departments/teams. Specific guidance about the course conduct are found in the department`s summer practice manual.

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Fundamental concepts in machine learning; history of artificial neuron models; perceptron learning; gradient descent and its alternatives: multi-layer perceptrons; convolutional neural networks; recurrent neural networks.

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Concepts of data mining. Data preprocessing. Data warehousing and OLAP for data mining. Association, correlation, and frequent pattern analysis. Classification. Cluster and outlier analysis. Mining time-series and sequence data. Text mining and web mining. Visual data mining. Industry efforts and social impacts. Applications of data mining.

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Overview of propositional and first-order logic. Computational aspects of logic: definite clauses, resolution, unification, and clausal forms. Modal, temporal, and other non-standard logics. Applications of various logics in computer science.

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Introduction to data communications. OSI Reference Model. Physical layer. Electrical interface and data transmission. Data link layer. Media access sublayer. LAN/MAN technologies. Network layer. Inter networking. Bridging and routing. Transport layer. Introduction to upper layers issues.

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Introduction to data communications. OSI Reference Model. Physical layer. Electrical interface and data transmission. Data link layer. Media access sublayer. LAN/MAN technologies. Network layer. Inter networking. Bridging and routing. Transport layer. Introduction to upper layers issues.

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Object-Oriented Programming Concepts. Exception handling. I/O Streams and Decorator Pattern. Concurrency. GUI Development. Security Issues. Objects over Networks. Database Connectivity. Serialization and Deserialization. Remote Method Calls. Introduction to Enterprise Components.

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Formal description and classification of programming languages. Specifications syntax. The parsing problem. Top-down and bottom-up parsing. Attaching semantics to syntax. Translator writing systems. Translator writing case study.

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Features and facilities provided by scripting languages; systems programming and interfacing with other programs; object oriented design and programming; concurrent programming with scripting languages; web application development, server side and browser side scripting.

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Information systems life cycle. Effective human communications. Common tools for information gathering. Some classical tools. Some structured tools. Organizational structure and personnel for information systems department. Computer systems evaluation and selection. Modeling and simulation. An information systems development methodology. A Case tool.

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Development of a complete software system based on available reference architectures; best practices in development; contemporary development environments and tools; architectural styles; concurrency; design patterns; libraries and frameworks;development of interface mechanisms including services and APIs;GUI design and implementation;design for reusability; component implementation; unit,component and system testing

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Fundamentals of software architecture and its modeling. Non-functional attributes of software as architectural considerations. Views, styles/patterns based organization of software architecture. Best practices to design and document the architecture of software-intensive systems. Architecture centric software development approaches. Software architecture assessment methods.

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Basic topics in robotics. Representing position, orientation and motion. Basic models of mobile robot motion. Forward and inverse kinematics for open-chain structures. Velocity relationships and the jacobian. Simple joint-level control. Principles of motion planning, trajectory construction and tracking. Robot vision.

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Basic LISP programming; picture analysis WALTZ algorithm; game playing, game trees, the mini-max rule, alpha-beta pruning technique; natural language understanding, transformational grammar, ATN grammars, techniques used in semantics.

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Introduction to linguistic theory and techniques used in natural language processing (NLP). Template and keyword systems. Declarative and procedural approaches to NL parsing. Phrase Structure. Unification-based grammar. Parsing algorithms. Semantics. Morphology and Lexicon.

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This course covers computatioanl techniques for mining the large amount of information produced by recent advances in biology, such as genome sequencing and microarray technologies. Main topics of the course include: DNA and protein sequence alignment, phylogenetic trees, protein structure prediction, motif finding, microarray data analysis, gene/protein networks.

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Image transforms. Image acquisition. Image representation and data structures. Image analysis. Image enhancement. Pattern recognition basics. Edge detection and segmentation.

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Local illumination models and surface rendering methods. Ray-tracing algorithm and speed-up techniques. Radiosity lighting model and radiosity method. Adding surface detail: Texture mapping, bump mapping, and procedural texture mapping. Three dimensional object representations: Polygon meshes, parametric cubic curves, parametric bicubic surfaces, quadratic surfaces, surface of revolution, sweep surfaces, fractal-geometry methods. Volume rendering. Color models. Introduction to computer animations.

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Introduction to simulation as a general scientific problem solving technique. Methodology of simulation and use of computers. Classifications of simulation models. Introduction to simulation programming languages.

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Hardware and software components of graphics systems. Output and filled-data primitives. Fourier analysis, convolution, sampling, quantization, aliasing. 2D and 3D geometric transformations. Two-dimensional viewing. Three-dimensional viewing: Viewing pipeline, viewing parameters, projections, viewing transformations, clipping. Visible surface detection. Introduction to illumination models and surface rendering.

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History and basic concepts of parallel computing. Classification of parallel processing systems. Organization of data and parallel storage. Design and analysis of parallel and vector algorithms. Performance and complexity of parallel algorithms. Examples and applications of some parallel algorithms.

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Image formation, camera models and parameters, stereo vision, shape from stereo, shape from single image cues, apparent motion, optical flow, introduction to 3D shape representation and recognition.

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Internet protocols.Wireless mobile and sensor networks.Software-defined networks.Network virtualization.Multimedia networking. Performance analysis of networks. Network management.Emerging topics in computer networks.

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Fundamental concepts in quantum computing; quantum mechanics for computer scientists; quantum circuits and quantum instruction set; quantum programming languages; quantum algorithms; error correction and fault tolerance.

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Guided research in computer science and engineering. Creative problem solving. Experimental and/or theoretical validation. Research and publication ethics.
Academic paper writing and presentation.

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Computer security concepts. Evolution of cryptography. Symmetric encryption algorithms. Public key cryptography. Authentication and access control. Database security. Malicious software. Intrusion detection. Software security. Secure programming. Operating systems security. Network security. Legal and organizational aspects of computer security. Advanced topics in computer security.

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Analysis, requirement specification and design phases of a computer system. Issues related to project design and presentation. Engineering ethics. Projects will be inspired from real life hardware/ software problems and students are expected to come up with a professional quality design solution by applying computer and software engineering methods. This course is the first one of the two courses (491 and 492) that collectively constitutes the senior project design. This first course mainly concentrates on analysis and design, presentation and engineering ethics.

Course Content

Analysis, requirement specification and design phases of a computer system. Issues related to project design and presentation. Engineering ethics. Projects will be inspired from real life hardware/software problems and students are expected to come up with a professional quality design solution by applying computer and software engineering methods. This course is the second one of the two courses (491 and 492) that collectively constitutes the senior project design. In this second course, students mainly concentrate on implementation and testing of the system based on the design the produced in the first course (CENG-491)

Course Content

These code numbers will be used for technical elective courses which are not listed regularly in the catalog. The course contents will be announced before the semester commences.

Course Content

Object Oriented Programming concepts, classes and objects, using constructors and set-get methods, method overloading, inheritance and polymorphism, java language elements.

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Cloud basics. Service models. Cloud platforms. Cloud access. Resource virtualization. Virtualization types. Multi-tenant software. Cloud applications: design, construction and deployment. Use of workflows. MapReduce. Data in the cloud. CAP theorem. NoSQL.

Course Content

These code numbers will be used for technical elective courses which are not listed regularly in the catalog. The course contents will be announced before the semester commences.

Course Content

These code numbers will be used for technical elective courses which are not listed regularly in the catalog. The course contents will be announced before the semester commences.

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This course provides a broad introduction to machine learning. The topics include supervised and unsupervised learning, Bayesion inference/classification, regression, clustering, kernels and Support Vector Machines (SVM) and accompanying concepts such as model and feature selection, combining classifiers such as boosting, active learning, dimension reduction techniques.

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Program of research leading to M.S. degree, arranged between a student and the faculty member. Students register to this course in all semesters starting from the beginning of their second semester while the research program or write-up of the thesis is in progress.

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Fuzzy set, fuzzy logic, fuzzy relations, fuzzy control, fuzzy rules and fuzzy database modeling. Fuzzy databases modeling with respect to physical databases, indexing, integrity constraints, nested databases, and object-oriented databases. Fuzzy logic applications in special non-traditional databases such as multimedia databases, video databases, spatial and spatio-temporal databases, and sensor network databases.

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Coverage of market structure, trading instruments, processes and trading in general, including necessary background and terminology. Presentation and illustration of electronic trading infrastructure, software tools, netwroking protocols, data structures and order execution. Theoretical and practical analysis of financial time series data. Building the necessary statistical framework for algorithmic decision making. Detailed coverage of basic and advanced trading strategies including high-frequency techniques and related requirements. Illustration of portfolio management schemes. Informative discussion of global and local markets and associated risks, compliances and regulations.

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Modeling systems, linear time properties, linear temporal logic, computational tree logic, model checking, abstraction techniques, state-space explosion problem, model-checking tools.

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Introduction to transmission and networks. Antennas, multipath propagation, frequency hopping. Satellite communication. Overview of current systems for cellular for networks, wireless LANs, mobile IP, Ad Hoc networks, the Bluetooth technology and the IEEE802.11 standard.

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Introduction to data mining and data warehousing, overview of relational data model and query languages, data warehousing and OLAP technology, data preparation, association rule mining, data mining and data confidentiality, classification and prediction, clustering, data mining under uncertainty, mining complex types of data, Web mining, multi-relational data mining.

Course Content

Basics and principles of cryptography and computer network security, their applications in the Internet and wireless networks, conventional (symmetric) and public-key (asymmetric) cryptography, cryptographic hash functions, message authentication codes and digital signatures, mutual trust and user authentication, transport and network layer security in the Internet, wireless network security, provable security, and advanced topics in security.

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Extensible Markup Language (XML) technologies; Semantic Web; RDF; DAML+OIL; Web Ontology Language (OWL); Software Agent Technology; FIPA Standards and JADE; Business-to-Business Interoperability Standards; EDI; RosettaNet; UDDI; ebXML; Web services; Application Servers; P2P Computing; JXTA; Grid systems.

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Predicate logic, proof techniques, set theory, functions, relat,ons, graphs. Formal languages, grammars, finite-state machines.

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Combinators. Semantic dependencies and lambda calculus. Variable elimination. Syntacticizing the combinators. Systems with and without logical forms. Choice of universal dependencies. Computation with combinators (combinatory parsing). CCG as a linguistic theory. CCG as a computational theory. Radical lexicalization. Models and applications.

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Basics of data communication, and computer networks, ISO/OSI basic reference model. physical, data-link, network, and transport layers. Routing, flow control, congestion control. Internetworking. TCP/IP suite of protocols. Higher layer protocols. Contemporary network architectures.

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Basics of advanced computer architecture. Multi-processing, multi-threading. Instruction level parallelism. High performance interconnection techniques. Memory hierarchy and shared memory models. Advanced caching techniques, cache coherency. Input/output performance issues. System performance parameters and analysis

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Basics of distributed computing systems. Global state management indistributed computing systems. Communication in distributed systems. Inter Process Communication and remote procedure call. Distribute filesystems. Fault tolerance. Synchronization and deadlocks. Load balancingand process migration. Distributed Operating Systems issues. Project(s).

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UNIX internals, device driver development. Network interfacing, client/server and Daemon applications. Process structure, file system, memory management, terminal I/O, network interface, character and block device drivers, graphic interfaces.

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Review of 2D basics, 3-D models and geometric transformations. Graphics standards (PHIGS, GKS) and user interface standards (XWindows). Solid modeling. Illumination and shading. Fractal models. Animation.

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Analysis of syntactic and semantic properties of programming languages. Operational, denotational and axiomatic approaches to semantics. Typed lambda calculus. Algebraic data types. Case studies include a typed functional language with higher-order functions and an imperative sequential language.

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Software quality. Quality assurance. Quality metrics. Construction of SQA. Quality management and control. SQA planning. Quality standards. Software testing techniques. Black box and white box testing. Test strategies, Unit Test, and Integration Test.

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Introduction to object-oriented programming languages, programming with Smalltalk, programming with C++.

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Logic and logic programming in understanding the semantics of databases. Extension of relational databases to deductive databases. Techniques for the processing of deductive database queries.

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Design of software systems and hybrid engineering systems. Software Engineering: Classical and modern methods. Comparative analysis of software development paradigms. Abstract design paradigm: Top-down approach starting with abstract entities. Hybrid system design in integration perspective. Application of formal systems to system development. Process models for software development. Design level process modeling.

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Overview of relational databases, the theory of relational database design, query processing, transaction management. Object-oriented databases, distributed databases, deductive databases. Other recent research topics such as data mining, multimedia databases, spatial databases and internet databases.

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Fundamentals of concurrent and distributed programming. Critical section problem and mutual exclusion. Concurrent programming primitives including semaphores, monitors. Synchronous communication by channels including the ADA rendezvous and remote procedure calls. Linda model. Distributed mutual exclusion and termination. Fault tolerance.

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An overview of distributed databases, a review of databases, concurrency control in centralized DBMSs, concurrency control in distributed databases, serializability theory 3, two-phase locking, timestamp ordering serialization, graph checking, time interval technique, optimistic methods, handling replicated data multiversion data, distributed database design, recovery query processing in distributed DBMSs.

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Structured approach and structured methodologies. Formal methodologies. Tools for information systems development. At least one commercially available methodology and relevant CASE tools to be used.

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Symmetric ciphers: classical and modern. The Data Encryption Standard (DES). Authentication. Key management. Asymmetric (Public Key) ciphers. Digital signatures.

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Problem solving and search strategies. Game playing. Knowledge Representation. Expert systems and rule chaining. Vision. Natural language processing. Machine translation. Machine learning. Neural networks.

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Paradigms of machine learning. Inductive, deductive, abductive forms of learning. Cognitive aspects of learning. Connectionist models of learning. Programming environments for learning programs.

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An introduction to the machine recognition of 1, 2 or higher dimensional patterns. Statistical and linguistic approaches. Survey of application areas. Bayes Decision Theory. Decision boundaries, classifiers, and discriminant functions. Estimation of parameters. Clustering. Feature selection. Structural approaches to P.R.. Neural networks recognizers. Applications.

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Turing machines. Church-Turing thesis. Decidability. Reducibility and Recursion Theorem. Complexity theory: Classes P and NP, NP-Completeness. Space Complexity. Intractability.

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Discrete time signals, and systems. Sampling, reconstruction, quantization. Digital image representation. Digital image fundamentals. Image transforms. Image enhancement. Image restoration. Image segmentation and description.

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Introduction to algorithms. The computational complexity of algorithms. Amortized analysis. Lower and upper bound theory. Approaches for designing algorithms: Divide-and-Conquer, Greedy Approach, Dynamic Programming, Backtracking and Branch-and-Bound. NP-Complete and NP-Hard problems. Approximation algorithms.

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Basic concepts and techniques. Knowledge representation. Drawing inferences. Tools and languages for expert systems. Knowledge engineering and expert systems development. Knowledge acquisition. Current expert system applications.

Course Content

Learning and generalization. The basic perceptron and linear separability. Multilayer perceptrons and the backpropagation algorithm. The Hopfield model and its dynamics. Bidirectional associative memory. Recurrent networks. Unsupervised learning and self-organizing maps. The counter- propagation network. Boltzman machine and simulated annealing. Recent advances.

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Introduction to algorithms and data structures for geometric problems in two and three dimensions. Convex hull; triangulations and planar subdivisions; geometric search and intersection; Voronoi diagrams; Delaunay triangulations; line arrangements; visibility.

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Accuracy in numerical analysis. Survey and critical comparison of numerical methods for matrix inversion. Systems of linear algebraic equations and eigenvalue problems. Systems of nonlinear equations. One and two dimensional interpolation and numerical approximation Numerical differentiation and integration. Selected algorithms will be programmed for solution on computer.

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Difference equations. Numerical solution of initial value problems in ordinary differential equations and stability of methods. Numerical solution of linear, nonlinear boundary value problems in ordinary differential equations. Numerical solution of parabolic, hyperbolic and elliptic partial differential equations. Selected algorithms will be performed for solution on computer.

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Multivariate statistical analysis with applications especially in the field of Computer Engineering. Review of introductory concepts in statistics. Hypothesis testing. Regression analysis. Discriminant analysis. Principal component analysis. Factor analysis. Applications with the use of existing computer packages.

Course Content

Fundamental theoretical concepts of discrete simulation. A selected simulation language to be taught. Overview of analog computer simulation. Review of basic probability and statistics. Selecting input probability distribution, random number generators, output data analysis for a single system, statistical techniques for comparing alternative systems, simulation languages and GPSS.

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Types of optimization problems in a variety of fields; efficiency of common methods of solution. Definition of optimization, extrema of functions of n variables, linear programming and the simplex method, nonlinear programming, and application of common methods to optimization problems.

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Classification of computers and introduction to parallel architectures. Pipelining and vector processing. Organization of data and parallel storage. Design and analysis of parallel algorithms. Performance measures of parallel algorithms. Examples of parallel algorithms. Programming assignments for parallel solution of some problems on the PVM and the Transputer system.

Course Content

Concurrency and distribution in AI: Agents: micro and macro views. Agent communication languages. Rational agency: economic/game theoretic, logical. BDI architecture. Multi-agent real-time search. Reinforcement learning. Multi-agent learning. Opponent modeling.

Course Content

Geometric reasoning, temporal reasoning, uncertain reasoning, non-monotonic reasoning, induction, metaknowledge and metareasoning, state and change, planning, intelligent agent.

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Edge detection and contour extraction. Region segmentation. Perspective projection and camera calibration. Matching and stereo. Projective geometry. Three dimensional reconstruction. Dynamic scene analysis.

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Computational aspects of linguistic theories. Human sentence processing. Interpretation. Information Structure. Ambiguity resolution. Interactions in multi-component grammars.

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History of Autonomous robotics: Braitenberg´s vehicles, Walter Grey Walter´s tortoises. Locomotion and sensing of robots. Robot control paradigms and architectures. Hierarchical paradigm: STRIPS, RCS. Reactive paradigm; Arbib´s schemas, Brooks´ subsumption architecture, behaviors, potential fields. Hybrid paradigm: AuRo, SSS, RAP´s. Mobile robot navigation: Localization, Map-making, SLAM, route planning. Robot vision. Evolution in autonomous robot systems. Sensory-motor coordination and learning. Multi-robot systems: Robot teams, Robocup, swarm robotics.

Course Content

Light and color; dynamic range; HDR image capture; HDR image and video storage; HDR image display; tonemapping; human visual system; image-based lighting.

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Introduction to digital geometry processing pipeline, which involves reconstruction, analysis, manipulation, visualization, and fabrication of geometric data. The practical applications implemented throughout the course content will make the student comfortable in engineering, medical, scientific visualization, entertainment (VFX/games), and many other related disciplines that make use of mesh models.

Course Content

(i) Research methods, namely how to formulate a hypothesis and pose a research question for it, how to design and perform experiments for investigating a research question, how to analyze the results of an experiment and draw conclusions; (ii) research and publication ethics; namely, the ethical issues that arise in conducting research and writing academic texts; (iii) how to write academic texts; namely the style, the organization, and the outline of academic texts such as conference papers, journal articles and theses.

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This seminar course must be taken by all M.S. students working towards their M.S. thesis. The students taking the course are required to make presentations on their thesis studies and prepare progress reports and final reports.

Course Content

Basic and advanced techniques for text-based information systems, text indexing, Boolean and vector space retrieval models, language models, tolerant retrieval, evaluation and interface issues, web crawling and link-based algorithms, ext clustering and LSI, neural models for retrieval.

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This course covers recent developments and open research problems in the area of Bioinformatics. Main topics include:
• Multiple sequence alignment,
• Protein folding problem, prediction of secondary/tertiary structure,
• Multiple structural alignment, protein docking,
• Functional classification of proteins, human genome annotation,
• Statistical modeling of biological data, kernel based methods, hidden Markov models,
• Data integration
• Prediction and mining of genetic networks and protein interaction networks.

Course Content

This is a follow up course to CENG 564 Pattern Recognition and covers new or renewed topics of research such as neural networks, structural/syntactic pattern recognition, ensemble classifiers, bayesian networks, markov random fields. In addition, application areas such as image recognition and speech analysis will be discussed.

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Course Content

Fundamental theoretical issues related to real-time computing. Characteristics of sequential, concurrent and real-time programs. Multiprocessing and multiprogramming. Introduction to real-time operating systems. Some examples of prominent real-time operating system and specifics of embedded development tools. Board support packages and device drivers. Real-Time scheduling and schedulability Analysis. Uniprocessor scheduling. Multiprocessor scheduling.

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Introduction to Web Application Architectures; DOM Level 1; Cascading Style Sheets (CSS); Service Oriented Web Architecture; Web Services Specification; P2P Architectures; Grid and Agent Based Architectures; MASHUP Architectures; B2B Document Standards.

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Advances in association rule mining, clustering, classification algorithms, spatio-temporal data mining, recommendation, mining on social nets, multi-relational data mining, and other contemporary issues in data mining.

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Basic notions and theory of digital forensics examination including computer and network forensics. Analysis techniques and special considerations that the examiner should make for a varity of file and operating systems. Networking investigations from several perspectives such as protocols and devices.

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Introduction to parallel computing platforms and programming models; implementatiton and performance evaluation of parallel algorithms; message passing: point-to-point communication operations and collective communication operations; shared address space programming using threads; example parallel applications.

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Course Content

Course Content

Metamodel hierarchy, model-to-model and model-to-text transformations. OMG`s Model-Driven Architecture. Metamodeling, model transformation and model management environments. Domain-specific languages: design, implementation and use. Practical applications of model-driven approach.

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Introduction to sparse matrix computations, algorithms for sparse linear systems and eigenvalue problems, efficient implementations for sparse matrix computations, related data structures.

Course Content

Manipulation in humans and robots, Rigid body motion; spatial displacements and rotation; Euler angles, rotation matrices, quarternions, screws and wrenches. Manipulator kinematics; forward and inverse kinematics, manipulator Jocabian. Rigid body statics; contact models, wrench cones, line of force and moment labelling. Friction; Coulomb s Law, friction cones. Quasi static manipulation; pushing. Grasping; force- and form closure, grasp synthesis, map, stability and quality. Planning; configuration space, probabilistic roadmaps, radiply exploring random trees, single and multi-query planning, trajectory optimization and human robot interaction.

Course Content

Statistical and Data Intensive methods for natural language processing. Language models, statistical machine translation. Information retrieval, text categorisation. Word sense disambiguation.

Course Content

Coverage of market structure, trading instruments, processes and trading in general, including necessary background and terminology. Presentation and illustration of electronic trading infrastructure, software tools, networking protocols, data structures and order execution. Theoretical and practical analysis of financial time series data. Building the necessary statistical framework for algorithmic decision making. Detailed coverage of basic and advanced trading strategies including high-frequency techniques and related requirements. Illustration of portfolio management schemes. Informative discussion of global and local markets and associated risks, compliances and regulations.

Course Content

Course Content

Basic concepts of locomotion, review of mathematical methods for modeling and analysis, free-body analysis, Lagrangian dynamics, dynamical stability, limit cycles, traditional wheeled and tracked robot morphologies, kinematic legged locomotion, zero-moment-point algorithms, neural control concepts, coordination of limbs and joints, exoskeletons, dynamic legged locomotion, reduced models, passive dynamic walking, spring-mass running models, hybrid zero-dynamics, snake robots, locomotion based on non-holonomic dynamics, aerial and underwater locomotion

Course Content

Learning and development in humans. Studies in developmental psychology and cognitive science. Novelty, curiosity, and surprise. Social interaction as a means for learning and development. Perception, learning and use of affordances in humans. Language development in children. Robotics as a test-bed for ideas developed in psychology and cognitive science. Review of recent studies in developmental robotics.
Machine learning in robotics. Supervised, unsupervised and reinforcement learning. Markov decision process framework to formulate and study learning problems in robotics. Challenges that robotics poses for using reinforcement learning methods. Inverse reinforcement learning. Apprenticeship learning. Learning from demonstration.

Course Content

Introduction to big data, big data platforms; storage and retrieval architectures for large-sale data; machine learning algorithms and tools for big data analytics; data stream features and real-time analytics on data streams; graph representation of linked big data; large-scale graph analytics algorithms and tools.

Course Content

Technical writing essentials and good practices for engineers working in any field related to information and communication technologies, in particular, computer and software engineers. Ethical issues and intellectual property rights related to technical documentation.

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Technical presentation essentials and good practices for engineers working in any field related to information and communication technologies, in particular, computer and software engineers. Ethical issues and intellectual property rights related to technical presentations.

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Ray tracing; image synthesis, 3D graphics, physically based rendering; graphics algorithms and hardware.

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Deep generative modeling with Autoregressive models; Energy-based models; Adversarial models; Variational models.

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Introduction to wireless networks; ad hoc networking architectures; mathematical models of ad hoc networks, medium access control; routing protocols in ad hoc networks; multicast routing; transport layer protocols; cross-layer design, security in ad hoc networks; quality of service; energy management; wireless network sensors; Internet of things; cooperation for ad hoc networking; distributed algorithms for ad hoc networking; localization, time synchronization, topology control; mobility models; adcanced topics in ad hoc networks such as vehicular ad hoc networks or flying ad hoc networks

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Properties of quantum computational systems and their mathematical representations. Basic quantum theory. Quantum mechanical principles, superposition, entanglement, interference. Quantum algorithms. Quantum teleportation. Superdense conding. Quantum computational complexity classes. Quantum error correction. Quantum finite automata. Quantum programming languages.

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Exploratory data analysis; experimental design; model prediction; the representation of text by linguistic features and deep learning of words, sentences and documents; textual similarity. Underlying structure of large language models; possible use cases and existing challenges for large language models.