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SYLLABUS INTELLIGENT COMPUTATIONAL TECHNIQUES Faculty of Mathematics and Natural Sciences / Chair of Computer Science COURSE TITLE FACULTY/INSTITUTE
COURSE CODE DEGREE PROGRAMME FIELD OF STUDY COMPUTER SCIENCE DEGREE LEVEL FORMA STUDIÓW/
STUDY MODE 2
Full-­‐time studies COURSE FORMAT
YEAR AND SEMESTER NAME OF THE TEACHER 1 year, semester I Prof. Zbigniew Suraj, PhD, DSc COURSE OBJECTIVES A student obtains a theoretical understanding of the subject and the skill of solving simple problems coming from the design of intelligent systems and data analysis. Programming, Artificial Intelligence, Operating Systems, Probability Theory and Statistics, Mathematical Analysis, Linear Algebra.
PREREQUISITES Knowledge: (X1A_W01) LEARNING OUTCOMES A student knows basic Artificial Intelligence techniques. Moreover, a student knows different ways of knowledge representation, basic algorithms from rough sets, fuzzy sets and Petri nets. Skills: (X1A_U01, X1A_U03, X1A_U06, X1A_U08) A student is able to prepare data in a way required by Data Mining algorithms. A student knows how to apply rough set (fuzzy set, Petri net) methods for solving basic problems coming from intelligent system domain and data analysis. COURSE ORGANISATION – LEARNING FORMAT AND NUMBER OF HOURS TIMETABLE Lecture: According to the schedule -­‐ (30 hours) Classes: According to the schedule -­‐ (45 hours) COURSE DESCRIPTION Lecture: Introduction: an overview of the fields of Artificial Intelligence and Concurrency. Rough sets: review of ordinary sets and relations, information tables and attributes, approximation spaces, knowledge representation systems, case study and comparisons with other techniques. Fuzzy systems: fundamentals of fuzzy sets, basic fuzzy set relations, basic fuzzy set operations and their properties, fuzzy logic fundamentals, fuzzy control basics, a note on fuzzy control expert systems. Classical Petri nets: basic concepts, particular Petri nets, properties of Petri nets, graph of markings and coverability tree, linear algebra, reduction methods. Non-­‐classical Petri nets: basic concepts, particular non-­‐classical Petri nets, examples of computer systems supporting the net representation of knowledge and the modeling of approximate reasoning. Petri nets and production rule systems: a toy production rule system, a data oriented Petri net model, control oriented models. Classes: Rough sets: Data analysis by using the Rosetta system. Fuzzy systems: Matlab system in the modeling of fuzzy control expert systems. Petri nets: examples of own computer systems supporting the modeling and analysis of concurrent systems (PN-­‐tools, PNES, ROSECON). Non-­‐classical Petri nets: examples of own computer systems supporting the net knowledge representation and the modeling of approximate reasoning (PNES). METHODS OF INSTRUCTION REQUIREMENTS AND ASSESSMENTS GRADING SYSTEM Lectures supported by slides and specialized software.
Lecture: In order to pass a lecture one needs: • to prepare a multimedia presentation (e.g. using PowerPoint application) related to the chosen topic • to pass the final test Grades: Local grade: ECTS grade: 5 A (excellent) 4.5 B (very good) 4 C (good) 3.5 D+(plus sufficient) 3 D (sufficient) 3-­‐ D (insufficient) 2 E (poor)
Classes: Points to gain: • Presence record – max. 10 points • Homework – max. 20 points (5 homework each for 4 points) • Tests – max. 30 points (2 tests each for 15 points) • Maximum score – 60 points Grades: Local grade: ECTS grade: 5 A (excellent) 4.5 B (very good) 4 C (good) 3.5 D+(plus sufficient) 3 D (sufficient) 3-­‐ D (insufficient) 2 E (poor)
TOTAL STUDENT WORKLOAD NEEDED TO Lecture: 80 ACHIEVE EXPECTED LEARNING OUTCOMES Laboratory: 50 EXPRESSED IN TIME AND ECTS CREDIT ECTS – 6 POINTS Polish/English (dependently on needs)
LANGUAGE OF INSTRUCTION INTERNSHIP MATERIALS
– TEXTBOOK AND REQUIRED MATERIALS: 1. K. J. CIOS, W. PEDRYCZ, R.W. SWINIARSKI, DATA MINING. METHODS FOR KNOWLEDGE DISCOVERY, KLUWER ACADEMIC PUBLISHERS, BOSTON 1998. 2. FUZZINESS IN PETRI NETS, J.CARDOSO, H. CAMARGO (EDS.), PHYSICA-­‐VERLAG, HEIDELBERG 1999. 3. R. DAVID, H. ALLA, PETRI NETS AND GRAFCET. TOOLS FOR MODELLING DISCRETE EVENT SYSTEMS, PRENTICE HALL, NEW YORK 1992. 4. T. MUNAKATA, FUNDAMENTALS OF THE NEW ARTIFICIAL INTELLIGENCE. BEYOND TRADITIONAL PARADIGMS, SPRINGER, BERLIN 1998.