SYLLABUS
COURSE TITLE
FACULTY/INSTITUTE
COURSE CODE
DEGREE PROGRAMME
FIELD OF STUDY
COMPUTER SCIENCE
COURSE FORMAT
YEAR AND SEMESTER
NAME OF THE TEACHER
INTELLIGENT COMPUTATIONAL TECHNIQUES
Faculty of Mathematics and Natural Sciences
/ Institute of Computer Science
DEGREE LEVEL
FORMA STUDIÓW/
STUDY MODE
2
Full-time studies
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 in what way
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 and RSES systems.
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, etc.).
Non-classical Petri nets: examples of own computer systems supporting the net knowledge representation and
the modeling of approximate reasoning (PNES, APNES).
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)
3D (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)
3D (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.