Erasmus ECTS Information Package

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Erasmus ECTS Information Package Faculty of EEEA 0572 Distributed Systems ЕCTS credits: 5 Weekly classes: 2lec+0sem+0labs+2ws Assessment: exam Type of exam: written Responsible department: Dept. of Computing, Faculty of Electrical Engineering, Electronics and Automation Lectors: Dr. Milen Loukantchevsky – Dept. of Computing, tel.: +359 (0) 82 888 674, Email: mil@ieee.org Summary: The course main goal is to help students reach more deep understanding of basic principles and practical aspects of the distributed computer systems. The basic structural components of the distributed systems are defined along with several fundamental models. The organization of the communications and interactions between distributed objects as well as control of the distributed resources and security are investigated. Syllabus Contents: Main terms. Properties. Distributed systems structuring. Distributed models. Communications. Communication protocols between distributed objects. System clocks synchronization. Logical clocks. Naming, identification and addressing. Atomic actions. Distributed transactions. Distributed file system. Fault-tolerance. Distributed systems security and protection. Teaching and Learning Methods: The lectures introduce main theoretical topics. Each group of lectures ends with conclusion of material and formulation of problems. At the workshops simulations and real systems are used putting lectures to practice. Each workshop begins with formulation and analysis of problems. At the very end the students are asked to summarize in written form their results. The information materials needed are given in electronic form to all of the students. 0575 Artificial Intelligence Systems ECTS credits: 5 Weekly classes: 2lec+0sem+0labs+2ws Assessment: exam Type of exam: written Departments involved: Department of Computing Faculty of Electrical Engineering, Electronics and Automation Lecturers: Assoc. Prof. Miroluyb Ivanov Mladenov, PhD, Department of Automatics and Mechatronics , phone 888 747, E-mail: mladenov@uni-ruse.bg Assoc. Prof. Svetlana Stefanova, PhD, Department of Computing, phone 888 356, E-mail: sstefanova@ecs.uni-ruse.bg Abstract: The course “Artificial Intelligence Systems” has the objective to acquaint the students with the basic problems, concepts and methodologies, used in the artificial intelligence systems, as well as areas of their application. Questions, related to development of expert systems, systems for pattern recognition, as well as the application of neuron networks and genetic algorithms in the systems with artificial intelligence are studied. The emphasis is on the different aspects for application of the artificial intelligence systems. Course content: Basic characteristics of artificial intelligence systems. Expert systems. Pattern recognition. Neuron networks. Genetic algorithms. Teaching and assessment: The lectures include the basic theoretical problems in the subject area, which are illustrated with examples in the communication technique. Different problems, connected with topics from the lectures are solved in the practical exercises. The workshops are conducted in a computer room. The term is validated, if all the topics from the practical exercises are solved and the lessons are attended regularly. The final mark is formed on the basis of the written exam. 253
Erasmus ECTS Information Package Faculty of EEEA 0579 Modelling and Simulation ECTS credits: 5 Weekly classes: 2lec+0sem+0labs+2ws Assessment: continuous assessment Type of exam: written and oral Department involved: Department of Computing Faculty of Electrical Engineering, Electronics and Automation Lecturers: Assoc. Prof. Tsvetozar Stefanov Georgiev, MEng, PhD, Dept. of Computing, tel.: 888 827, E-mail: TGeorgiev@ecs.uni-ruse.bg Principal Assistant Georgi Stefanov Georgiev, MEng, Dept. of Computing, tel.: 888 681, E-mail: GStefanov@ecs.uni-ruse.bg Abstract: The course objective is to give the necessary theoretic knowledge and practical skills to create models and simulate different systems including computer ones. Course prerequisites: Higher Mathematics, Discrete structures and modelling, Digital signal processing and Computer systems for control. Course content: Introduction to modelling and simulation. Technology for computer modelling. Mathematical formalization and abstract description in computer modelling. Automata description of systems. Functional modelling. Analytical modelling. Statistical modelling. Computer simulation of processes and systems. Teaching and assessment: At the lectures students are familiarised with the theoretic aspects of the main topics connected with modelling and simulation. At the practical sessions they work with specific environments for modelling and simulation. During the semester the students have two tests which contain practical exercises solved on PC and theoretical questions. The final mark is based on the marks of the two tests and the student’s participation in the workshops. 0585 Intelligent Teaching Environments ECTS credits: 5 Weekly classes: 2lec+0sem+0labs+2ws Assessment: exam Type of exam: test Departments involved: Department of Computing Faculty of Electrical Engineering, Electronics and Automation Lecturers: Assoc. Prof. Dr. Irina Ilieva Zheliazkova; Department of Computing, Eng, MSc, PhD; tel: 888-744 E-mail: Irina@ecs.uni-ruse.bg Abstract: The course gives theoretical knowledge and practical skills for design, implementation, and experimentation of intelligent multimedia WINDOWS- and WEB-based environments for teaching different cognitive types of knowledge. It has input relationships with the bachelor degree courses: “Discrete structures and modelling”, “Software engineering”, “Integrated environments”, “Artificial Intelligence”, “Programming in INTERNET” and “Multimedia technologies in teaching”; parallel relationships with the master degree courses ”Artificial intelligence systems”, “Web design”, and “Systems for business modelling” and output relationship with the theses project. Course content: Formulating and solving the problem for creating task-oriented environments (TOEs) for e-teaching. Administration in an integrates environment for individualized planned teaching. Modelling queries to base of different types of tasks. Adaptive and intelligent TOE for knowledge testing. TOE for teaching lectures. TOЕ for teaching algorithmic knowledge. TOE for teaching structural knowledge. TOE for training in dynamic systems. Teaching and assessment: For each ТОЕ of the lecture material the theoretical background, language for knowledge description, UML diagrams, and the tools architecture are considered. During the exercises students acquire the technology of using the TODE. Then they work as developers and programmers for extending its functionality, improving the user interface or integrate a programming module into different TODEs. The results of the student teams are assessed by the assistant. The final assessment of the course is formed as an average assessment of the practical exercises and a test on the lecture material. 254
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Erasmus ECTS Information Package

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