Wydział Matematyki i Nauk Informacyjnych PW academic year: 2014/2015 Course: Program/Semester: Status of the course: Responsible: Contact information: Hours per week, Assesment method ANDROID APPLICATION DEVELOPMENT Computer Science BSc, sem.5 (winter) Elective dr inż. Marcin Luckner mluckner@mini.pw.edu.pl Lc / Ex / L / P 1/-/2/- Course code --- ECTS 4 Objective: 1. Student gets knowledge required to develop market-ready application from scratch. 2. After the course students without assistance can keep learning how to create more advanced applications. Course description: 1. IDE/SDK introduction, Android Architecture. 2. Basic application structure and lifecycle. Connecting code with resources. 3. Multiple activities. Saving settings. Background tasks. 4. Network (Wi-Fi Direct, NFC, Bluetooth, Sockets). Files, dialogs, toasts. 5. Android Services. Android Broadcast Receivers. 6. Background services. 7. Content providers. Supporting multiple devices. 8. 2D/3D Graphics 9. Third source services. 10. Application publishing 11. Monetizing your application. 12. Meetings with professional developer. Required prerequisites: Java (Programming 3) Recommended: Assesment regulations: Final grade is based on a project created during the course.
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Wydział Matematyki i Nauk Informacyjnych PW academic year: 2014/2015 Course: Program/Semester: Status of the course: Responsible: Contact information: Hours per week, Assesment method ASSEMBLERS Computer Science BSc sem.5 or 7 (winter) Elective dr hab. inż Jacek Misiurewicz jmisiure@elka.pw.edu.pl, tel 5441, room 447 WEiTI Lc/ Ex/ L / P 2 / 0/ 1/ 0 credit Course code --- ECTS 4 Objective: Insight to most popular microprocessors family architecture and assembly language: Intel x86, ARM. Ability to hybrid assembly language with high level language programming to improve program performance. Ability to program at the bare hardware level. Course description: Lectures: 1. Introduction to assembly languages. 2. Intel x86 microprocessors family architecture: registers, memory addressing modes, flags. 3. Intel x86 microprocessor instruction set: data transfer instructions, integer arithmetic, logical operations, shifts and rotations, jumps, loops, procedures and stack operations. 4. Assembly macro language. 5. Hybrid programming: manual optimization of inner loop, binding assembly language subroutines with C/C++, pure assembly executable interacting with OS. 6. Floating point unit architecture and instruction set. 7. Other architectures: ARM (Advanced RISC Machine) and examples of other ones 8. Specific features accelerators in different architectures. Tutorials: - Laboratories: (7x2 hours, some subjects take 2x2 hours) 1. Intel x86 simple assembly inner loop in a complex program 2. Intel x86 C/C++/OS interacting 3. Linux kernel driver in assembly language 4. Raspberry Pi (ARM) assembly program under Linux 5. Raspberry Pi bare bones program interacting with hardware Project: - Required prerequisites: Programming 1 (Fundamentals, C) Programming 2 (Object Oriented, C ++) Recommended: Operating Systems UNIX Fundamentals Assesment regulations: Labs contribute 50% to the total score Mid-term test: 25% End-term test: 25%. Lab assignments must be done during the semester (no redo during the session). A student must be present at least at 80% of lab exercises to be scored. Signature
Faculty of Mathematics and Information Science Acad. year 2014/2015 Course title: Discrete random processes. Analysis and simulation Field of the study/semester Computer Science BSc/Msc sem. (winter/summer semester) Course status Elective Responsible people: Dr hab. eng. Paweł J. Szabłowski Tel: Telephone, e-mail: E-mail: pjsz@hotmail.com Hours per week Lc / E / L / P 2 / 1 / / 1 Code No ETCS 5 Course description: After necessary review of basic notions of probability theory and technic of simulation of sequences of i.i.d. observations, the lectures present first properties of simple branching processes. Next students are acquainted with a few properties of Poisson processes as well as with their generalizations like nonhomogeneous and composed Poisson processes. Then they are acquainted with two models of queuing systems: so called M/M/c with and without a queue and their characteristics. Finally we discuss basic properties of renewal processes including justification of so called excess time paradox. During tutorials they learn how to calculate certain number characteristics of the processes presented. Finally during laboratories they are supposed to simulate some of the processes they were acquainted with during the lectures and observe some of their properties. Required prerequisities: Calculus, Probability Reference books: 1. Sheldon Ross. 'Introduction to Probability Model s'. Harcourt Acad. Press, N.Y. 2000 2. L. Kosten, 'Stochastic Theory of Service Systems ', Pergamon Press, N.Y. 1973 3. Geoffrey Grimmett and David Stirzaker, 'One Thousan d Exercises in Probability', Oxford University Press, N.Y. 2001. Assesment method: During the semester students are supposed to a. Participate in lectures b. Take part in 6 laboratory exercises and submit 5 re ports c. Take part in auditory exercises. c. Take part in final exam Each Laboratory report is graded from 0 to 4 points. Final exam will be graded from 0 to 20 points. Hence one can score 40 points at most. Points are c onverted to grades according to the following algorithm: 21-24 C 25-28 C+ 29-32 B 33-36 B+ >36 A.. (signature)
Wydział Matematyki i Nauk Informacyjnych PW academic year: 2013/2014 Course: ENTERPRISE APPLICATIONS IN.NET FRAMEWORK Program/Semester: Computer Science BSc; sem. 5 Status of the course: elective Responsible: Contact information: Hours per week, Assesment method Karol Walędzik, MSc k.waledzik@mini.pw.edu.pl Lc / Ex / L / P 2/-/2/- credit Course code --- ECTS 4 Objective: Students of the course should become familiar with the most important contemporary concepts and technologies employed when developing.net Framework based enterprise applications for MS Windows system. Course description: The course should provide students with general knowledge about most important architectural choices and technologies available for relational database access and data manipulation (including object-relational mapping), business logic layer, communication in distributed environment and presentation layer implementation. Since students are expected to already be familiar with desktop application development solutions, more emphasis will be put on web-based technologies. While most of the course will concentrate on technologies delivered as part of the.net Framework, selected most popular external libraries (both developed at Microsoft and by third parties) will be briefly described as well. Laboratories will give students a possibility to demonstrate their practical understanding of the concepts presented during the lecture. Lectures: Most important enterprise applications architectural and design concepts, including: o good practices of object-oriented programming; o layered architecture and basic patterns for each application layer. Data access and manipulation technologies, including: o ADO.NET; o object-relational mapping; o LINQ-based technologies. Business logic implementation approaches, including: o most common architectural patterns; o Windows Workflow Foundation. Communication technologies, including: o Windows Communication Foundation; o XML web services basics. Web-based presentation technologies, including: o ASP.NET; o ASP.NET MVC; o Silverlight. Unit testing, including overview of Visual Studio Unit Testing Framework. Laboratories: Laboratories will consist of 3 or 4 tasks in the form of mini-projects developed in teams of two. Each project will demonstrate the students' proficiency with at least one selected.net Framework based technology. Each developed solution and its architecture will also have to be presented to other students.
Required prerequisites: Object oriented programming Programming 4 (Windows Programming) Relational databases Assesment regulations: The tasks performed as part of the laboratories will constitute the sole base for the final grade. Each mini-project will be scored based on the amount of technology proficiency demonstrated by its authors, its technical quality, design quality, adherence to good programming principles and, last but not least, quality of the presentation prepared for other course students. Any delay in project development will also negatively influence its score. Karol Walędzik
Wydział Matematyki i Nauk Informacyjnych PW academic year: 2014/2015 Course: Program/Semester: Status of the course: Responsible: Contact information: Hours per week, Assessment method INTRODUCTION TO EMBEDDED SYSTEMS Computer Science BSc, sem.5 Computer Science MSc - winter Elective Dr Piotr Zbigniew Wieczorek THE FACULTY OF ELECTRONICS AND INFORMATION TECHNOLOGY, ROOM 259, PHONE 22 234 7336, EMAIL: PWIECZOR@ELKA.PW.EDU.PL Lc / Ex / L / P 2/0/1/0 Course code --- ECTS 4 Objective: During course students get general information and knowledge on embedded system issues i.e. various architecture types, implementation techniques in modern microcontrollers and programmable circuits. Students also get familiar with the use of embedded systems in commercial and professional applications. Structural programming practice based on embedded systems and System on Chip solutions. Introduction to basic standards and requirements of embedded systems in industry i.e. automotive Practical skills in selection of particular embedded systems, their configuration, and implementations adapted to special applications. Course description: Definition of an embedded system and its advantage over a standard microprocessor based system. Differences in programming resulting from real time approach, specific hardware and operating system requirements. During course students familiarize themselves with specific issues on I/O peripherals connected to embedded systems such as LCDs, OLED displays, touchpads, ADC-DAC converters, servomotors. Some part of the course will be focused on the feedback between embedded system and the environment (i.e. control of servomotors according to feedback loop data from sensors and DAC-ADC converters). Lectures: Description of embedded systems philosophy and architecture based on modern microcontrollers. Explanation of typical parameters, capabilities, and limitations of single-chip microcontrollers and their comparison to typical microprocessors (peripherals integration, differences in I/O operation and control). Some practical (commercial) examples of modern embedded systems. Practical issues on connecting input/output (IO) devices to microcontrollers, examples of devices allowing the system to communicate with the environment. Data acquisition with use of ADC s, and the description of simple sensors and actuators. Basic information on microcontrollers communication systems TWI, SPI, I2C, RS485/232, and wireless standards. During lectures some examples of use of software tools for programming and configuration of embedded systems will be shown. Debug tools for embedded systems: online vs. offline debug techniques will be also discussed. Detailed hardware and software practical issues discussed during lectures: Real time and discrete time in embedded systems; Interrupts handling; Signals acquisition and processing; Information interchange between systems; Synchronization; Multitasking.
Hands-on activities during lectures e.g. USB software implementation on Texas Instruments embedded board are also provided. Tutorials: Laboratories: During the laboratory activities students perform practical programming exercises on evaluation boards (STM, TI, and Atmel). Practical programming issues during laboratories might focus on: o IO devices/interfaces (LCD, touchpads etc.); o DC servo operation, actuator implementation; o Measurement of physical quantities with use of sensors integrated in an embedded system; o Implementation of a simple system with the physical feedback e.g. a simple robot which gathers information from sensors; Laboratories will be performed in pairs. Each laboratory stand will consist of a PC computer, development board with an embedded system, a DC supply, and a multimeter. Project: Required prerequisites: o Skills in structural programming preferably C language (Ansi C, GCC), o Skills in basics of electronics and physics, o Skills in basics of digital systems: logical gates, registers, memories (RAM, ROM), understanding of operation of a simple microprocessor and its particular parts (ALU, registers) Recommended: 1. The definitive guide to the ARM Cortex-M3. Joseph Yiu 2. C programming for the absolute beginner. Michael A. Vine 3. Configurable logic microcontroller : nonvolatile memory ATMEL products. Atmel Corporation,1998 Assessment regulations: Students are obliged to obtain at last 26 points to pass the course. Assessment contains of points collected during laboratories (max. 30 points) and a short written test (max. 20 points). Laboratories are supervised and graded. Each of five laboratories allows for collecting 0-6 points. Calculating of final mark is based on the sum of points collected during the semester. Signature
Wydział Matematyki i Nauk Informacyjnych PW academic year: 2014/2015 Course: Program/Semester: Status of the course: Responsible: Contact information: Hours per week, Assesment method LINUX FOR EMBEDDED SYSTEMS Computer Science BSc/MSc, sem.5 Computer Science MSc - winter Elective Wojciech Zabołotny, PhD ZUiSE, WEiTI, 7717, wzab@ise.pw.edu.pl Lc / Ex / L / P 1/0/2/0 E Course code --- ECTS 4 Objective: Course description: Lectures: Introduction - Linux as OS for embedded systems Typical problems related to embedded systems differences between Linux for typical server or PC and Linux for an embedded system busybox and uclibc Building Linux for an embedded system possible approaches Adjustment of an existing distribution Building system from the scratch Linux bootoladers uboot, grub etc., different techniques for booting of development and production versions of the system Adaptation of the bootloader for particular hardware platform Use of Linux kernel with kexec feature to build more advanced loader Environments for building of Linux embedded systems OpenWRT,Open Embedded and Buildroot Presentation of Buildroot environment. Compilation of the simplest system for an emulated demonstration platform Optimization of the Linux kernel for the embedded system Proper selection of filesystems for an embedded system Selection of applications in the Buildroot environment to realize system with required functionalities Adjustment of the Buildroot environment and kernel for the particular hardware platform (board) Adding of own/additional applications to the Buildroot environment User interface in Linux based embedded systems Communication with displays, buttons, keyboard etc. Control via TCP/IP network Control via network or Bluetooth connection from the mobile phone Debugging of the Linux system on an embedded platform. Testing of the kernel and user-space applications Optimization of the system: problems specific for an embedded system, configuration minimizing risk of system corruption due to unexpected shutdown or power failure, optimization of FLASH memory wear. Laboratories: (10 3-hour sessions, 5 topics, each topic in 2 sessions the 1 st : introduction, the 2 nd : evaluation)
Introduction to the development session used in the laboratory Buildroot based compilation of the basic Linux system and starting it on the target platform Testing of different possibilities of system booting (internal FLASH memory, SD card, external USB disc, loading of the system image via network) Compilation of the system for an embedded system with required functionality (e.g. print server, multimedia server, etc.), its configuration and testing Compilation of the system with additional software added by the student. Testing of the system. Compilation and testing of the system optimized for use of particular peripheral devices. Configuration of the Linux system with OpenWRT environment, its compilation and testing on the development platform. Required prerequisites: Recommended: Assesment regulations: The final grade is based on the points obtained from the exam at the end of the semester (ca. 40% of points) and grades from laboratory sessions (ca. 60% of points). To pass the course student must earn at least 50% of points. Signature
Wydział Matematyki i Nauk Informacyjnych PW academic year: 2013/2014 Course: PARALLEL PROCESSING Program/Semester: Computer Science BSc, sem. 5 Status of the course: Elective Responsible: Contact information: Hours per week, Assesment method...dr Felicja Okulicka-Dłużewska okulicka@mini.pw.edu.pl... Lc / Ex / L / P 1/-/2/- exam Course code --- ECTS 4 Objective: The aim of the course is to give the knowledge on the basic rules of parallel programming and standard libraries Posix and MPI. The algorithms of distributed system management are presented. Additionally the parallel versions of numerical methods introduced for computation speed-up are given. After the course the student should: be able to implement a parallel algorithm using Posix be able to implement a parallel algorithm using MPI have knowledge on the algorithms of the distributed system management know the difference between the parallel and sequential numerical algorithms. Course description: Lectures: 1. Parallel machines. Basic definitions: process, thread, parallelizm 2. Synchronization problems: producer-consumer, readers-writers, 5 philosophers. 3. Processes synchronization in Posix. 4. Algorithms of scheduling 5. MPI Standard 6. Net distributed algorithms 7. Parallel linear algebra algorithms. Laboratories: 1. Processes synchronization in Posix. 2. Programming with MPI: process communication (blocking, unblocking, synchronized, asynchronized, point-to-point and collective) virtual topologies data structures in MPI groups of processes 3. Implementation of net distributed algorithms 4. Implementation parallel linear algebra algorithms. Required prerequisites: Unix, programming in C Recommended: 1. Barney, B., Introduction to Parallel Computing, https : //computing:llnl:gov/tutorials/parallel com p/ 2. Barney, B., Message Passing Interface (MPI), ht tps : //computing:llnl:gov/tutorials/mpi/ Assesment regulations: Student can have 50 point on labs and 50 point during exam. Only students who has at least 26 point after labs can attend the exam. To pass exam 26 point from 50 should be reached.
The final mark is the sum of the points from lab and exam. Signature
Wydział Matematyki i Nauk Informacyjnych PW academic year: 2014/2015 Course: PROLOG - LOGIC PROGRAMMING AND APPLICATIONS Program/Semester: Computer Science BSc/MSc sem. 5 Status of the course: elective Responsible: dr inż. Andrzej Mazurkiewicz Contact information: andrzej@mazurkiewicz.org Hours per week, Assessment method L / T / Lab / P 2/0/2/0 credit/exam Course code --- ECTS... Objective: to gain basic skills on Prolog programming as well as to see how can we apply it to practical problems in AI. In addition students should understand the theory underpinning the theoretical and practical logic programming. Course description: Lectures: 1. Deducibility, refutation, and inference rules. Introduction to programming in PROLOG (part 1). 2. Propositional and predicate logic. Introduction to programming in PROLOG (part 2).. 3. Modes, logical consequence, and satisfiability. Introduction to programming in PROLOG (part 2). 4. Clauses, Horn Clauses and SLD- resolution. Introduction to programming in PROLOG (part 3). 5. Lists and symbolic arithmetic. 6. Graph searching. 7. Applications of PROLOG to expert systems. 8. Solving with PROLOG logic puzzles. 9. Interfacing PROLOG with other software (C/C++, Python, Java, R, etc) 10. Interfacing PROLOG with other software (continued). 11. SLD-trees as a representation of the PROLOG program execution. 12. Soundness and completeness of SLD resolution. 13. More advanced PROLOG programming in AI. 14. Project discussion. Laboratories (linux with swi-prolog, C/C++, Java, Python-2, Python-3, R development environments): 1. Introduction to Programming in PROLOG. 2. Simple acyclic graph searching. 3. Symbolic arithmetic. 4. Lists and operations on lists. 5. Solving some logic puzzles. 6. Searching cyclic graphs (application to simulation and games). 7. Expert systems in PROLOG. 8. Interfacing PROLOG with other software.. 9. Database objects management and using information from existing databases. 10. Project work. Project: a) collection of simpler programs b) plus implementing in PROLOG a more advanced tasks (e.g. implementing database management operations as well as expert knowledge). Required prerequisites: basic skills in C/C++, Java, Python or other programming languages, basic relational database management skills, basic linux skills. Recommended: 1. The Art of Prolog, l. Sterling and E. Shapiro, MIT. 2. Prolog Programming for AI, I. Bratko, Addison Wesley. 3. Programming in Prolog, W. F. Clocksin and C. S. Mellish, Springer-Verlag. 4. www.postgresql.org
5. www.swi-prolog.org Assessment regulations: project 25%, labs 25% and exam 50% Marks: a) 51-60 pts 3.0, b) 61-70 pts 4.0, c) 71-100 pts 5.0 Signature
Wydział Matematyki i Nauk Informacyjnych PW academic year: 2014/2015 Course: Program/Semester: Status of the course: Responsible: Contact information: Hours per week, Assessment method AGENT-SEMANTIC SYSTEMS AND APPLICATIONS Computer Science MSc, first or second semester Computer Engineering BE, 6 th or 7 th semester (matching the above) can be offered jointly to both groups elective Marcin Paprzycki, Ph.D., D.Sc. Maria Ganzha, Ph.D., D.Sc. marcin.paprzycki@ibspan.waw.pl Lc / Ex / L / P 2 / 0 / 1 / 1 Semester-long project Course code ECTS 4 Objective: The aim of the course is to introduce students to basic theoretical and practical issues involved in: (a) developing (distributed) agent systems (b) development of ontologies and their applications in semantic data processing (c) development of hybrid agent-semantic systems Course description: Lectures: 1. Introduction: software agents and agent systems Basic definitions and metaphores Critical analysis Software angents in applications Existing agent platforms; comparative analysis Formal and semi-formal methods for agent system modelling; Agent Modeling Language (AML) Methodologies and tools for agent system development JADE: agent system scalability experimental results 2. Ontologies and semantic data processing Basic definitions Languages for ontology demarcation: RDF, RDFS, OWL Overview of existed ontology: Dublin Core, FOAF, CYC, etc. Semantic databases: query languages. Semantic Web Reasoning, reasoners and languages 3. Applications 1. Personal agent case studies: Travel support system User profile: ontology, weighting preferences Supporting user decisions on the basis of semantic data processing Agents in virtual organization Semantic user profile Supporting user decisions on the basis of semantic data processing Agent-based decision support system for glider pilots
Utilization of software agents to collect, intelligently combine and apply in a context specific way data originating from multiple sources (including sensor data) Software agents as an approach to effectively combine multiple existing software artifacts Software agents as Grid / cloud middleware Agent-based system skeleton Ontologies in the system Development of agent-semantic system Laboratory: 1. JADE agent platform Platform structure and offered services Hello World, creation of the first agent Foundations of agent communication: message structure, Agent Communication Language (ACL) Agent mobility JADE Android agents residing on mobile devices 2. Ontologies and semantic data processing Protégé ontology creation Querying semantic data. Reasoning. Reasoning engines 3. Advanced agent communication involving ontology Project: Students select the project during the second meeting. The results of the project are: presentations, technical reports, working and well documented code. It is expected that the best projects can end-up as conference presentations and publications. It is also possible that research can be continued and extended to become the MS Thesis. Required prerequisites: - Object oriented programming (Java preferred) - Mathematical logic - Software Engineering Recommended literature: 1. Developing Multi-Agent System with JADE; F. Bellifemine, G. Caire, D. Greenwood, John Wiley & Sons, 2007 2. Developing Multi-Agent System with JADE; F. Bellifemine, G. Caire, D. Greenwood, John Wiley & Sons, 2007 3. Mohammad Essaaidi, Maria Ganzha, Marcin Paprzycki Software Agents, Agent Systems and Their Applications, IOS Press, 2012 4. Multiagent Systems and Applications: Volume 1: Practice and Experience, Ganzha, Maria and Jain, Lakhmi Berlin, Springer, 2013, Volume 45. XX, 278 p 5. Texts available at: http://www.ibspan.waw.pl/~paprzyck/mp/cvr/research/agent.html 6. Explorer s Guide to the Semantic Web, T. B. Passin, MANNING, 2004 7. JADE documentation, http://jade.tilab.com/ 8. Protege Documentation, http://protege.stanford.edu/ 9. Internet-based resources Assessment regulations: Students will be evaluated on three aspects of the project: (i) presentations, (ii) technical report, (iii) documented, working code.
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Wydział Matematyki i Nauk Informacyjnych PW academic year: 2014/2015 Course: Program/Semester: Status of the course: Responsible: Contact information: Hours per week, Assessment method AGENT SYSTEMS AND APPLICATIONS Computer Science MSc, first or second semester Computer Engineering BE, 6 th or 7 th semester (matching the above) can be offered jointly to both groups elective Maria Ganzha, Ph.D., D.Sc. Marcin Paprzycki, Ph.D., D.Sc. marcin.paprzycki@ibspan.waw.pl Lc / Ex / L / P 2 / 0 / 1 / 1 Semester-long project Course code ECTS 4 Objective: The aim of the course is to introduce students to basic theoretical and practical issues involved in design and implementation of software agents and (distributed / mobile) agent systems Course description: Lectures: 1. Introduction: software agents and agent systems 2. Agent frameworks/platforms 3. Basic approaches to development and management of software agents and multi-agent systems 4. Agent system development methodologies 5. Applications of software agents and agent systems 5.1. Software agents as Grid / cloud middleware 5.2. Personal agents case studies: 5.2.1. Personal agents supporting needs of travelers (agent-semantic system) 5.2.2. Personal agents supporting workers in a virtual organization (agent-semantic system) 5.2.3. Agent-based decision support for glider pilots (agent-sensor system) 5.3. Agents in smart grids / micro grids 5.4. Agents in management of network resources 5.5. Agents in e-commerce Laboratories: 1. JADE agent platform 1.1. Platform structure and offered services 1.2. Hello World, creation of the first agent 1.3. Foundations of agent communication: message structure, Agent Communication Language (ACL) 1.4. Agent mobility 1.5. JADE Android agents residing on mobile devices Project: Students select the project during the second meeting. The results of the project are: presentations, technical reports, working and well documented code. It is expected that the best projects can end-up as conference presentations and publications. It is also possible that research can be continued and extended to become the MS Thesis. Required prerequisites: - Object oriented programming (Java preferred)
- Mathematical logic - Software Engineering Recommended literature: 1. Developing Multi-Agent System with JADE; F. Bellifemine, G. Caire, D. Greenwood, John Wiley & Sons, 2007 2. Mohammad Essaaidi, Maria Ganzha, Marcin Paprzycki Software Agents, Agent Systems and Their Applications, IOS Press, 2012 3. Multiagent Systems and Applications: Volume 1: Practice and Experience, Ganzha, Maria and Jain, Lakhmi Berlin, Springer, 2013, Volume 45. XX, 278 p 4. Texts available at: http://www.ibspan.waw.pl/~paprzyck/mp/cvr/research/agent.html (and subpages) 5. JADE documentation, http://jade.tilab.com/ 6. Internet-based resources Assessment regulations: Students will be evaluated on three aspects of the project: (i) presentations, (ii) technical report, (iii) documented, working code. Signature
Faculty of Mathematics and Information Science 2010/2011 Course title COMPUTER FORENSICS Internal code no Program BSc Course type lectures Number of credit points 4 Placement (recommended) Placement in timetable summer semester (6 th ) Length 1 semester Hours per week 2/0/1/0 Status of the Course in the study program Elective Objective To deliver baselines (practical, bottom-oriented knowledge) of sound computer forensics practices enabling information technology and information security professionals to ensure the overall integrity and survivability of their IT infrastructure. Courses description Introduction to baselines of computer forensics - definitions, needs, requirements, legal and ethical aspects; investigation phases - preparations and start of an investigation, case study, analysis of evidence, documentation. The discovery, recovery, preservation, analysis and control of electronic evidence, presentation standards. Tools of trade ( (TCT, Sleuthkit, Autopsy, CF-oriented Linux distributions, solutions for other platforms, commercial tools, ediscovery). Booting processes, start disks, boot sectors and partitions, system loaders, preparing and using bootable CD/DVD and USB images. File systems (FAT, NTFS/NTFS5, EXT2/EXT3, USF1/USF2 etc.) - specifications, data structures, investigation techniques. Identifying data types, reconstruction and analysis of files and data areas in search for evidence, interpretation of system and application logs, proving break-ins. Investigation of live systems (Windows, Unix/Linux) and network data flows, searching for evidence at the Internet. Required prerequisites Electronic Principles Introduction to Digital Systems Operating Systems Computer Networks Assessment method Lectures: Two close notes tests in the middle and at the end of the semester (25 points each). 3 extra
points for attendance at lectures (roll-call at random 3 times during the semester). Labs: Five 3-hour labs starting with 10-minute short test, then strictly individual work with emphasis put on documentation. 5 x 10 points to earn. No retakes. No requirement to pass lecture tests and labs separately. Final score: Points earned at tests and labs sum up, min 51 points required to pass, linear grade scale (51-60 points for 3, 61-70 points for 3,5 etc., 91-103 points for 5). Reference books Harlan Carvey, Windows Forensic Analysis, SYNGRESS 2007 Keith J. Jones, Richard Bejtlish, Curtis W. Rose, Real Digital Forensics, Computer Security and Incident Response, Addison-Wesley 2006 Eoghan Casey, Digital Evidence and Computer Crime, Elsevier Academic Press 2004 Brian Carrier, File System Forensic Analysis, Addison-Wesley 2005 Barry J. Grundy, The Law Enforcement and Forensic Examiner's Introduction to Linux, LinuxLEO.com and other guides and manuals found at Internet repositories. Responsible person Magdalena Szeżyńska, Ph.D., CISA Signature
Faculty of Mathematics and Information Science Acad. year 2014/2015 Course title: Field of the study/semester Computer Science BSc/Msc sem. 6 BSc/1-3 MSc (summer semester) Course status Elective Responsible people: dr inż. Iwona Wróbel Telephone, e-mail: E-mail: i.wrobel@mini.pw.edu.pl Hours per week Course description: From Finite Element Method to Signal Analysis Lc / E / L / P 2 / 0 / 1 / 1 Code No ETCS 4 I. Finite element method and its applications 1. Finite elements, finite element space, simplex finite elements, rectangular finite elements. 2. Finite element interpolation and least-squares approximation. 3. Variational formulation of boundary value problems, the Lax-Milgram theorem. 4. Estimates for general finite element error. 5. Generalized (weak) derivatives, Sobolev spaces, examples of the Lax-Milgram problems in Sobolev spaces. 6. Finite element method for nonstationary problems. 7. Generating and solving linear systems in finite element method. 8. Applications of finite element method. II. Finite difference methods for initial value problems for ordinary differential equations 1. Deriving finite difference approximations. Local truncation error. Global error. 2. Stability, consistency, and convergence. 3. Euler's method, forward and backward. 4. Multistep methods. Runge-Kutta methods. 5. Variable step size methods. III. Finite difference methods for boundary value problems and initial value problems 1. Deriving finite difference approximations. Stability, consistency, and convergence. 2. Schemes for the boundary problem for the second order ordinary differential equation. 3. Schemes for the Dirichlet problem for the second order elliptic equation. 4. Schemes for the heat equation and the wave equation. IV. Splines 1. Linear, parabolic, and cubic splines. B-splines. 2. Spline interpolation. 3. Error estimation. 4. Splines in R 2. V. Numerical integration 1. Gauss type quadratures. 2. Clenshaw-Curtis quadratures. 3. Adaptive quadratures. 4. Numerical computation of indefinite integrals. 5. Error estimation. 6. Numerical integration in R 2. VI. Fast Fourier Transform (FFT) and its applications.
Required prerequisities: Linear algebra, Calculus, Differential Equations, N umerical Methods Reference books: 1. S. C. Brenner, L. R. Scott, The mathematical theory of finite element methods, Springer 2008, 2. P. G. Ciarlet, The finite element method for elliptic problems, North-Holland Pub. Comp., Amsterdam 1978 (SIAM, Philadelphia 2002), 3. G. Dahlquist, A. Björck, Numerical methods, New Jersey, 1974, 4. G. Golub, J. Ortega, Scientific Computing and Differential Equations, An Introduction to Numerical Methods, Academic Press, 1992, 5. D. Kincaid, W. Cheney, Numerical analysis, 3rd ed, American Mathematical Society, Providence, RI, 2002, 6. P. M. Prenter, Splines and variational methods, J.Wiley Pub.,New York 1989, 7. 12. G.Hammerlin, K-H. Hoffmann, Numerical Mathematics, Springer-Verlag 1991, 8. O. C. Zienkiewicz, The finite element method, McGraw Hill, London, 3rd ed., 1977, 9. O. C. Zienkiewicz, K. Morgan, Finite elements and approximation, J. Wiley & Sons, N.York, 1983. Assesment method: Final grade is based on the project and the test. The project must be completed before a fixed deadline. Any delay results in penalty points.. (signature)
Wydział Matematyki i Nauk Informacyjnych PW academic year: 2014/2015 Course: Network Operating Systems Program/Semester: Computer Science BSc/MSc, sem.4 (summer) Status of the course: Elective Responsible: Dr inż. Janusz Oleniacz (W.Fizyki PW) Contact information: oleniacz@if.pw.edu.pl, 501738337 Hours per week, Assesment method Lc / Ex / L / P 1/-/2/- Course code --- ECTS 4 Objective: Poznanie nowoczesnych sieciowych systemów operacyjnych oraz architektury nowych technologii opartych na usługach. Zdobycie doświadczenia w konfigurowaniu i administracji systemów i usług dla systemów z rodziny Linux i Microsoft Windows o różnej skali wydajności i roli klient lub serwer. Poznanie problemów związanych z ciągle rosnącymi potrzebami i wymaganiami jakości usług oraz metod ich rozwiązywania. Poznanie terminologii i standardów dla usług i protokołów sieciowych, jak też zagadnień bezpieczeństwa, wirtualizacji, administracji i testowania dla usług i sieciowych systemów operacyjnych, z uzupełnieniem o technologie gridowe i cloud computning. Course description: Wprowadzenie uporządkowanej, podbudowanej teoretycznie wiedzy ogólnej w zakresie architektury sieciowych systemów operacyjnych oraz technologii sieciowych, w tym usług sieciowych: 1. katalogowych (DAP/LDAP, Microsoft AD, Novell NDS) 2. innych, jak: DNS,DHCP, autentykacji, autoryzacji, uwierzytalniania (AAA) 3. warstwy aplikacji (e-mail,web,file, e.g. MS Exchange/sendmail, IIS/Apache, NFS) 4. multimedialnych (voice,video) oraz standardów protokołów i usług (IETF/RFC, ISO, ITU). Zapoznanie się z podstawowymi metodami, technikami i narzędziami stosowanymi przy rozwiązywaniu prostych zadań informatycznych z zakresu budowy systemów komputerowych, sieci komputerowych i technologii sieciowych oraz systemów wbudowanych, w tym zwłaszcza: 1. modelu OSI 2. architektury SOA, WCF (.NET), web-servisów (SOAP,WSDL etc) 3. nowych rozwiązań typu przetwarzania i usług w chmurze (cloud computing, Microsoft Azure itp.) 4. bezpieczeństwa systemów i usług (SE Linux, firewalle, IDS/IPS, iptables/isa Server, VPN, SSL/TLS/IPsec) 5. różnorodności systemów sieciowych (od najstarszych do najnowszych, mobilnych i eksperymentalnych- np.android,plan9) 6. technologii wirtualizacji i emulacji 7. wirtualizacji wszelkich zasobów jako podstawy technologii gridowych i chmurowych 8. testowania usług typu klient/serwer, klient/serwis, wydajności, zgodności 9. problemów integracji, współpracy i zarządzania (rola standardów) Lectures: 15h godzin wykładów z materiałami udostępnianymi przez stronę WWW przedmiotu.
Tutorials: Dokumentacja dostępna przez internet, materiały z konferencji technologicznych. Laboratories: Szereg ćwiczeń praktycznie pokazujący działanie poszczególnych technologii sieciowych NOS. Większość w oparciu o maszyny wirtualne, lub inne zasoby dostępne zdalnie. Project: Przygotowanie 2 prezentacji (10-15min), pierwsza opisująca plan i technologie, druga efekty praktyczne jego realizacji. Required prerequisites: Wstępna wiedza o sieciach komputerowych, internecie i protokołach TCP/IP. Recommended: Strony WWW przedmiotu, konferencji tematycznie związanych i producentów rozwiązań. Assesment regulations: godziny kontaktowe - 50h; w tym obecność na wykładach 15h obecność na laboratoriach 30h konsultacje 5h przygotowanie do zajęć 55h, w tym przygotowanie do wykładów 10h przygotowanie projektu i do zajęć laboratoryjnych 45h Razem nakład pracy studenta 105h = 4 pkt. ECTS Ocena : z zadań wykonywanych podczas laboratorium 60 % ocena z projektu 40 % Signature
Faculty of Mathematics and Information Science Acad. year 2014/2015 Distributed Operating Systems Course title: Field of the study/semester Computer Science BSc/Msc sem. (winter/summer semester) Course status Elective Responsible people: Telephone, e-mail: Hours per week Course description: Ewa Niewiadomska-Szynkiewicz, PhD, DSc Adam Kozakiewicz, PhD Tel: 234 3650 E-mail: ens@ia.pw.edu.pl Lc / E / L / P 2 / 0 / 1 / 0 DOS Code No ETCS 4 Provides students with the distributed operating systems design and implementation. Lecture scope: 1. Introduction do distributed operating systems: definition, goals and architecture, hardware and software concepts, modern architectures. 2. Processes and threads, software agents, processes migration, load balancing algorithms. 3. Communication: layered protocols, client-server and peer-to-peer models, RPC, RMI, messagepassing and stream-oriented communication. 4. Synchronization: physical and logical time, time synchronization, distributed snapshot, election algorithms, distributed mutual exclusion, distributed transactions. 5. Naming: distributed name spaces, location services, aliases-identifiers-addresses, redirection. 6. Consistency and Replication: data-based and client-based consistency models, consistency protocols. 7. Fault Tolerance: types of faults, redundancy, reliable communication & RPC, virtual synchrony, distributed commit. 8. Security: terminology, service isolation and minimization, access control models, trust management, introduction to cryptography, public key infrastructure, secure protocols. 9. Distributed file systems: file systems implementation (NFS, Coda, Lustre, GFS) 10. Cluster systems: attributes, types of clusters, cluster systems implementations MOSIX, SSI, Kerrighed, queuing systems PBS. 11. Grid systems: attributes, implementations Unicore, Condor, Globus. Laboratory: Design and implementation of a distributed system, concentrating on consistency and fault tolerance. Work in realistic programming teams (3-4 students). Required prerequisities: Operating systems, elementary knowledge of data bases. Reference books: 1. A. S. Tanenbaum., M. van Steen, Distributed Systems. Principles and Paradigms, Prentice Hall 2002, 2006.
Assesment method: Two tests in the semester. Attendance at laboratory. Solving a problem at the laboratory.. (signature)
Course title Component Programming with Java EE Internal code no JavaEE Program MSc Course type lectures, lab Number of credit points 4 Placement (recommended) 1-4 semester Placement in timetable winter semester Length 1 semester Hours per week 1/0/2/0 Written 06-01-2005 Revised 01-05-2013 Status of the Course in the study program Objective Courses description elective Provide the students with the knowledge regarding component-based multilayer systems that separate business logic from presentation layer and use Java Enterprise Edition to define programming components. Discuss enterprise level solutions for maintenance and integration of software systems. The need for multilayer systems. The pros and cons of clientserver paradigm. Enterprise Java Beans and application servers. Local and remote interfaces. Selected design patterns. Connection pooling and the role of application servers. Java Persistence API and EJB-QL. Session beans. Asynchronous processing using message-driven components. Queue and publisher/subscriber model. The use of JNDI to access server s resources. The use of web services. WSDL and SOAP standards. REST style services. Introduction to BPEL. Selected solutions of modern web interface. Model-View-Controller pattern. Required prerequisites Assessment method short assignments prepared during the labs (100 points), at least 51 points are needed to pass Reference books Graham, Al. O Callaghan, A. Cameron Wills, Object-Oriented Methods Principles & Practice, Pearson Publication Limited, 2001 Weerawarana S. et al, Web Services Platform Architecture, Prentice Hall, 2005 Burke B., Monson-Haefel R., Enterprise JavaBeans 3.0, Helion 2007 Responsible person Maciej Grzenda
Wydział Matematyki i Nauk Informacyjnych PW academic year: 2014/2015 Course: Program/Semester: Status of the course: Responsible: Contact information: Hours per week, Assesment method NONLINEAR SYSTEMS AND GRAPHICS APPLICATIONS Computer Science MSc; winter elective Prof. Stanisław Janeczko janeczko@mini.pw.edu.pl Lc / Ex / L / P Course code ECTS 4 2/-/1/- credit NSGA Objective: Provide the students with the basic notions and methods of nonlinear dynamical systems, catastrophe theory and bifurcations, creating the geometrical models for various phenomena in social, economical, physical and life sciences, understanding mathematical description of phenomena in administration, stock market and ecology. Course description: Lectures: 1. Introduction to the theory of geometric models. a. Gradient vector fields, potentials and parameter spaces. 2. Classification of simple singularities and their unfoldings a. Simple local singularities of functions of type A k, D k, E k. b. Unfoldings of singularities. Morsifications of degenerated critical points of functions. 3. Algorithms for recognition of stationary surfaces for simple models. a. General description of stationary surfaces. b. Methods of elimination theory, resultants and discriminants. 4. Graphics of stable bifurcation sets and their metamorphoses. a. Graphical analysis of "fold", "cusp", "swallowtail", "butterfly", "hyperbolic umbilic", "elliptic umbilic", and "parabolic umbilic" elementary bifurcation sets. b. Families of intersections of higher dimensional bifurcation sets. 5. Applications of graphical analysis of stable stationary surfaces to some introductory models. a. Catastrophes in the stock market. b. Predictions of qualitative changement in nonlinear systems. c. Social modelling, formation of global opinions and attitudes. d. Phase transitions in ordered and semi-ordered structures. e. Multiplicity of perception and brain modelling. f. Visible contours, caustics and critical focusing of beams of rays. 6. Introduction to fractals and nonlinear dynamics. a. Iteration of maps, fixed pints, periodic orbits, feedback phenomenon, b. Attracting and repelling fixed points, stable and unstable orbits. c. Dynamics of the quadratic family. d. Iteration in the complex plane, Q c (z)=z 2 +c. Mandelbrot set, Julia sets. 7. Application of fractals. a. Logistic maps, population dynamics. b. Chaos in physical and economical systems. 8. Computer modelling of interacting systems (interaction of fractals). 6. Applications of discrete dynamical systems. 8. Examples of numerical simulations and computer graphics perspective. Laboratories: Instructions for graphical projects and algorithms Required prerequisites: CS 102, CS 206 Recommended: T. Poston, I. Stewart, Catastrophe Theory and its Applications, Pitman, London 1978
M.C. Tangora, Experiment and Theory in Computers in Geometry and Topology, Lecture Notes in Pure and Appl. Mathematics, 114, 1989, Marcel Dekker. E.C. Zeeman, Catastrophe Theory, Selected Papers 1972-1977, Addison-Wesley 1977. S. Janeczko, Wybrane Zagadnienia Teorii Katastrof, Oficyna Wyd. PW, 1996. Assesment regulations: Test in the end of term - 2h, presentation of the project Signature
Graphic Processors in Computational Applications https://e.mini.pw.edu.pl/en/print/4467 1 TRIAL z 1 MODE a valid license will remove this message. See the keywords property of this PDF for more information. 2014-04-10 18:13 Warsaw University of Technology Faculty of Mathematics and Information Science Course title Graphic Processors in Computational Applications Course code Lecturer dr inż. Krzysztof Kaczmarski, mgr inż. Przemysław Zdroik ECTS 4 Course coordinator dr inż. Krzysztof Kaczmarski Academic year 2013-2014 Faculty unit Zakład Zastosowań Informatyki i Metod Numerycznych, MiNI PW Course type elective Programme bsc msc Mode of studies full time Course level basic Major computer science Speciality none Language of instruction English Semester (level 1) 6 Semester (level 2) 2 Assessment method credit Hours during semester 45 Teaching method Lect. Tut. Lab. Proj. Hours per week. 1 0 0 2 Standards Prerequisites Principles of parallel programming, C, C++ programming, algorithms and data structures Course content GPU architecture and comparison to CPU, multi-core processors, shared memory and cashe. Processes execution models: SIMD, MIMD, MISD, etc. CUDA nvidia library (CUDA lib, CUDA SDK) CUBLAS (BLAS) library. GPU algorithms: matrices and vectors operations, sorting, graphs searching and other graph algorithms, numerical methods. Objectives of the course Objective of this course is to learn architecture of multi-core processors their programming paradigm and applications. This course is based mostly on nvidia GPUs and CUDA library. Assessment regulations All projects are divided into three groups with different difficulty and credit. Each student has to prepare exactly two projects from the list. Grades: 50-60: 3, 61-70: 3.5, 71-80: 4.0, 81-90: 4.5, 91-100: 5.0. Recomended reading and software 1. CUDA ZONE Portal http://www.nvidia.com/object/cuda_home.html 2. CUBLAS Library http://developer.download.nvidia.com/compute/cuda/2_0/docs/cublas_library_2.0.pdf 3. GPU Gems 3 Hubert Nguyen Addison-Wesley Professional (August 12, 2007) ISBN 0321515269 4. Patterns for Parallel Programming, Timothy G. Mattson, Beverly A. Sanders,Berna L. Massingill Addison-Wesley Professional; 1 edition (September 25, 2004) ISBN: 0321228111 5. Introduction to algprithms, Thomas H. Cormen et al. 6. Any literature on parallel programming and supercomputers [date] [signature] Project is co-financed by European Union within European Social Fund
Oracle Database Administration https://e.mini.pw.edu.pl/en/print/4493 1 TRIAL z 2 MODE a valid license will remove this message. See the keywords property of this PDF for more information. 2014-04-10 18:16 Warsaw University of Technology Faculty of Mathematics and Information Science Course title Oracle Database Administration Course code Lecturer mgr Rafal Maczewski ECTS 4 Course coordinator mgr Rafal Maczewski Academic year 2013-2014 Faculty unit Zakład Zastosowań Informatyki i Metod Numerycznych, MiNI PW Course type elective Programme bsc msc Mode of studies full time Course level moderate Major computer science Speciality none Language of instruction English Semester (level 1) 6 Semester (level 2) 2 Assessment method exam Hours during semester 30 Teaching method Lect. Tut. Lab. Proj. Hours per week. 2 0 2 0 Standards Prerequisites Databases Course content The course trains in tasks typical to Oracle database administration: administering database users, disk space, privileges, doing database backups recovering database after serious failures (for example: disk or computer failures) optimizing SQL statements tuning Oracle database At the end of the course students should be able to: Objectives of the course create and manage schema objects such as: tables, views, indexes, sequences, synonyms create and manage tablespaces, move data and indexes between tablespaces, manage and optimize space used by a table or index identify database files: control files, data files, online redo logs, parameter file, archived logs do offline and online backup (backups when the database is open) use backup to recover the database after some files have been lost due to hardware failure or human error set up standby database system with two computers: primary database that is used by all users, and standby database which is tracking changes to the primary database and can be activated when the primary database fails analyze SQL statement execution plan, create indexes to optimize a way SQL statement is executed, and provide optimizer hints tune Oracle database parameters Assessment regulations Each laboratory except the first starts with a test assessing previously discussed topisc. The duration of the test varies between 15 and 90 minutes depending on the complexity of the task. During the test students can use their notes, books, access internet (with the exception of using email, instant messaging applications and other forms of communication). Each test is graded on the scale from 0 to 10 points. The remaining time on the laboratory is spent on practicing topics discussed on the lecture. In order to qualify for the exam, student must score more than 50% of points from all tests after excluding one test with the worst result. Students who score over 90% of points from all tests after excluding one worst result, are excused from the exam and get an "A" mark. The final exam takes place in the laboratory room. The final exam is graded on the scale from 0 to 100 points. The final mark is assigned based on the sum of points from the exam and all laboratories after excluding one worst result: over 90% of points - A over 80% of points - B+ over 70% of points - B over 60% of points - C+ over 50% of points - C 50% of points or less - Failed Recomended reading and software Oracle documentation available online at http://technet.oracle.com Toad freeware edition - freeware application that is useful to Oracle administrators and programmers [date] [signature]
Oracle Database Administration https://e.mini.pw.edu.pl/en/print/4493 2 TRIAL z 2 MODE a valid license will remove this message. See the keywords property of this PDF for more information. 2014-04-10 18:16 Project is co-financed by European Union within European Social Fund
Data Mining https://e.mini.pw.edu.pl/en/print/4445 1 TRIAL z 1 MODE a valid license will remove this message. See the keywords property of this PDF for more information. 2014-04-10 18:18 Warsaw University of Technology Faculty of Mathematics and Information Science Course title Data Mining Course code Lecturer dr inż. Krzysztof Bryś ECTS 4 Course coordinator dr inż. Krzysztof Bryś Academic year 2013-2014 Faculty unit Zespół Dydaktyczny, MiNI PW Course type elective Programme bsc msc Mode of studies full time Course level basic Major computer science Speciality none Language of instruction English Semester (level 1) 7 Semester (level 2) 3 Assessment method credit Hours during semester 45 Teaching method Lect. Tut. Lab. Proj. Hours per week. 2 0 1 0 Standards Prerequisites Computer Statistics Course content Data Mining model and methods Objectives of the course The course introduces principles and techniques of data mining. It emphasizes the advantages and disadvantages of using these methods in real world systems, and provides hands-on experience. Assessment regulations Lab: project (max 40 pts) max 20pts for documentation and max 20pts for computer implementation, preparation of data sets and tests, presentation. Lecture: multiple choice test (max 60pts) 20 questions, +3 pts for each correct mark, -3 pts for each wrong mark. Both parts (project and test) have to be passed. (at least 21 pts for the lab and at least 31 pts for the test). Final grade:51-60 pts = 3.0, 61-70 pts = 3.5, 71-80 pts = 4.0, 81-90 pts = 4.5, 91-100 pts = 5.0. Recomended reading and software 1. M. Berry, G. Linoff, Mastering Data Mining, John Wiley & Sons, 2000. 2. U. Fayyad, G.Piatetsky-Shapiro, P. Smyth, R.Uthurusamy, Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, 1996. 3. J. Han, M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 1996. 4. N. Indurkhya, S.M. Weiss, Predictive Data Mining: A Practical Guide, Morgan Kaufmann,1997. [date] [signature] Project is co-financed by European Union within European Social Fund
Wydział Matematyki i Nauk Informacyjnych PW r.ak. 2013/2014 Przedmiot: Kierunek/Semestr: Rodzaj przedmiotu Prowadzący: Zakład, telefon, E-mail: Tygodniowy wymiar godzin i sposób zaliczenia BIOINFORMATYKA Informatyka / studia II st. sem. letni Przedmiot obieralny dr Joanna Sasin-Kurowska e-mail: asia.sasin@gmail.com 1/0/2/0 Kod przedmiotu ECTS 4 Cel przedmiotu / efekty kształcenia: Bioinformatyka to interdyscyplinarna dziedzina, której celem jest przetwarzanie i analiza danych biologicznych. Obejmuje ona budowę, rozwój i zastosowanie metod obliczeniowych, służących do badania struktury, funkcji, ewolucji genów, białek, jak również całych genomów. Ważnym celem bioinformatyki, szczególnie w ostatnich latach (w związku z coraz powszechniejszym zastosowaniem w biologii molekularnej tzw. technik wysokoprzepustowych), jest rozwój metod wykorzystywanych do zarządzania ogromnymi ilościami danych, zawartymi w biologicznych i medycznych bazach danych, oraz ich eksploracji (ang. data mining). W trakcie zajęć studenci zostaną zaznajomieni z metodami algorytmicznymi, technikami analizy porównawczej i statystycznej, które są stosowane do powyższych zagadnień. Program przedmiotu: Program wykładu: 1. Wprowadzenie. Formaty i pochodzenie analizowanych danych. Krótki zarys ich znaczenia biologicznego. Przegląd najważniejszych baz danych. 2. Analiza danych sekwencyjnych- algorytmy porównywania sekwencji, zastosowanie programowania dynamicznego, ukrytych łańcuchów Markowa, statystyczna ocena dopasowania sekwencji 3. Algorytmy szybkiego wyszukiwania informacji z sewkencyjnych baz danych 4. Ewolucja molekularna - odtwarzanie najbardziej prawdopodobnych ścieżek ewolucji na podstawie istniejących danych molekularnych. Metody klasyfikacji oparte na na rozkładach prawdopodobieństwa oraz na nieparametrycznej estymacji rozkładów prawdopodobieństwa. Zastosowanie drzew klasyfikacyjnych. 5. Najważniejsze metody do przewidywania struktur trzeciorzędowych i funkcji białek na podstawie sekwencji. 6. Analiza ekspresji genów. Zastosowanie metod rzutowania i wykrywania zmiennych ukrytych do analizy mikromacierzy. 7. Biologia systemowa. Algorytmy przewidywania i badania złożonych oddziaływań występujących w systemach biologicznych. 8. Metody eksploracji niesekwencyjnych baz danych, w tym danych bibliograficznych, klinicznych, struktur molekularnych czy ścieżek metabolicznych i oddziaływań pomiędzy cząsteczkami biologicznymi. 9. Wykorzystanie języków programowania do omawianych wcześniej zagadnień (Python/R). Program laboratorium 1. Wprowadzenie do biologicznych baz danych. 2. Wprowadzenie do języka programowania Python. 3. Budowa uliniowień sekwencji aminokwasowych i nukleotydowych z użyciem biblioteki BioPython. 4. Testowanie wybranego algorytmu szybkiego wyszukiwania informacji z sewkencyjnych baz danych z użyciem biblioteki BioPython
5. Zastosowanie metod klasyfikacji opartych na rozkładach prawdopodobieństwa oraz na nieparametrycznej estymacji rozkładów prawdopodobieństwa do odtwarzania ewolucji molekularnej. 6. Budowa modeli struktur trzeciorzędowych białek na podstawie sekwencji. 7. Wprowadzenie do środowiska R. 8. Analiza ekspresji genów. Analizy mikromacierzy z zastosowaniem bibliotek dostępnych w R. 10. Metody eksploracji niesekwencyjnych baz danych, w tym danych bibliograficznych, klinicznych, struktur molekularnych czy ścieżek metabolicznych i oddziaływań pomiędzy cząsteczkami biologicznymi. Przedmioty poprzedzające / wymagania wstępne: statystyka bazy danych algorytmy i struktury danych Literatura podstawowa: (zgodnie z wymogami Państwowej Komisji Akredytacyjnej wylistowana literatura musi znajdować się w zasobach biblioteki głównej PW, w przypadku braku danej pozycji należy ją zastąpić inną) Higgs P.G., Attwood T.K., Bioinformatyka i ewolucja molekularna, PWN, 2012 Xiong J., Podstawy bioinformatyki, WUW, 2009 Tramontano A., Introduction to bioinformatics, Chapman & Hall/CRC,cop. 2007 Regulamin zaliczenia przedmiotu: Zaliczenie laboratoriów z przedmiotu na podstawie rozliczenia z 3 projektów (60%). Zaliczenie wykładów na podstawie wyników kolokwium (40%).. (podpis)
Advanced Artificial Intelligence https://e.mini.pw.edu.pl/en/print/4422 1 TRIAL z 2 MODE a valid license will remove this message. See the keywords property of this PDF for more information. 2014-04-10 18:23 Warsaw University of Technology Faculty of Mathematics and Information Science Course title Advanced Artificial Intelligence Course code Lecturer dr hab. inz Jaroslaw Arabas ECTS 4 Course coordinator dr hab. inz Jaroslaw Arabas Academic year 2013-2014 Faculty unit Wydzial Elektroniki i Technik Informacyjnych, Instytut Systemow Elektronicznych, Zaklad Sztucznej Inteligencji Course type elective Programme bsc msc Mode of studies full time Course level advanced Major computer science Speciality ai Language of instruction English Semester (level 1) 7 Semester (level 2) 3 Assessment method exam Hours during semester 45 Teaching method Lect. Tut. Lab. Proj. Hours per week. 2 0 0 1 Standards Prerequisites Artificial Intelligence Fundamentals Lecture syllabus Course content 1. Definitions of learning tasks and optimization tasks. Learning from data: basic idea. 2. Supervised learning: nonlinear regression. Nonlinear models - multilayer perceptrons, RBF. 3. Methods to improve learning - bagging and boosting 4. Classification in Rn as a specific form of the regression task. Applying perceptrons for the classification 5. Support Vector Machine - linear version 6. Support Vector Machine - nonlinear version. Kernel SVM. 7. Learning classification for discrete data - overview of methods 8. Refinements of the ID3 methods - pruning decision trees. Random forests. 9. Memory beased models of regression and classification. k-nn classifier 10. Data clustering. k-means method 11. Linear Vector Quantization Scope of the project Students apply and test one of the methods introduced during the lecture. To formulate real-life problems as tasks of Artificial Intelligence To apply methods that are appropriate for the problem Objectives of the course To capture relationships between Artificial Intelligence and Databases, Desision Support Systems, Computer Aided Design To apply and test algorithms Grading is based on the total sum of points, where maximum is 100 Assessment regulations Up to 50 points can be achieved for the project, and up to 50 points - for the exam The exam is in a written form, takes 105 minutes, students are expected to solve several tasks of various grade of difficulty. Notes and books are allowed. Points are the base for the final grading according to the following rule: [range which contains number of points] ->grade 0-50->2, 51-60->3, 61-70->3.5, 71-80->4, 81-90->4.5, 91-100->5 G. Luger, Artificial intelligence Z. Michalewicz, D. Fogel: How to solve it: modern heuristics Recomended reading and software [date] [signature] Project is co-financed by European Union within European Social Fund
Advanced Artificial Intelligence https://e.mini.pw.edu.pl/en/print/4422 2 TRIAL z 2 MODE a valid license will remove this message. See the keywords property of this PDF for more information. 2014-04-10 18:23
Fractals https://e.mini.pw.edu.pl/en/print/4465 1 TRIAL z 2 MODE a valid license will remove this message. See the keywords property of this PDF for more information. 2014-04-10 18:24 Warsaw University of Technology Faculty of Mathematics and Information Science Course title Fractals Course code Lecturer dr Robert Małysz ECTS 4 Course coordinator dr Robert Małysz Academic year 2013-2014 Faculty unit MiNI PW Course type elective Programme msc Mode of studies full time Course level basic Major computer science Speciality none Language of instruction English Semester (level 1) Semester (level 2) 2 Assessment method credit Hours during semester 45 Teaching method Lect. Tut. Lab. Proj. Hours per week. 2 0 1 0 Standards Prerequisites analysis, linear algebra and geometry, programming, (measure theory, computer graphics, functional analysis, stochastic processes) Course content Classical fractals and definitions of dimension 1. Classical fractals (Sierpiński gasket, Cantor set, Koch curve, Julii sets) 2. the Minkowski dimension, packing dimension 3. the Hausdorff dimension, Hausdorff measure, properties of dimensions Deterministic fractals 4. IFS-Iterated function system IFS, the Hutchinson operator, the Banach theorem 5. Dimension of self-similar and self-affine fractals 6. Fractal interpolation functions, Peano curve, the Weierstrass functions 7. Dimension and properties of fractal interpolations functions 8. Fractal surfaces (e.g. bilinear fractal interpolation surfaces), properties and dimension 9. Julii sets and the Mandelbrot set Random fractals and self-similar stochastic processes 10. Random fractals, modifications of deterministic fractals 11. the Brown motions, fractional Brownian motions-fbm, self-similar processes 12. the Frostmann theorem, the Hausdorff dimension of self-similar processes and fractional Brownian motions Applications of fractal geometry 13. Fractal landscapes 14. Fractal compression 15. the Hurst exponent, applications of fractal geometry in economy and physics, scaling laws tbd Objectives of the course Assessment regulations Students will create computer programs based on lectures. The list of programs is on website www.mini.pw.edu.pl/~malysz Recomended reading and software 1. Barnsley, B. - Fractals everywhere, Acad. Press Inc., 1989. 2. Falconer, K. - Fractal Geometry: Mathematical Foundations and Applications. John Wiley & Sons, 2003. 3. Mandelbrot, B.B. - Fractals and Scaling in Finance. Springer 1997 4. Massopust, P - Fractal Functions, Fractal Surfaces, and Wavelets, Academic Press, 1995. 5. Peitgen, O., Jurgens, H., Saupe, D. - Fraktale PWN 1995 6. Peters, E.E. - Fractal Markets Analysis. John Wiley & Sons, 1994. 7. Skarbek, Wł. - Metody reprezentacji obrazów cyfrowych Akademicka Oficyna Wydawnicza 1993 [date] [signature]
Fractals https://e.mini.pw.edu.pl/en/print/4465 2 TRIAL z 2 MODE a valid license will remove this message. See the keywords property of this PDF for more information. 2014-04-10 18:24 Project is co-financed by European Union within European Social Fund