Administracja Centralna Uczelni - Wymiana międzynarodowa (S1)
Sylabus przedmiotu Knowledge Extraction from Data with Rough Set Method and its Applications:
Informacje podstawowe
| Kierunek studiów | Wymiana międzynarodowa | ||
|---|---|---|---|
| Forma studiów | studia stacjonarne | Poziom | pierwszego stopnia |
| Tytuł zawodowy absolwenta | |||
| Obszary studiów | — | ||
| Profil | |||
| Moduł | — | ||
| Przedmiot | Knowledge Extraction from Data with Rough Set Method and its Applications | ||
| Specjalność | przedmiot wspólny | ||
| Jednostka prowadząca | Katedra Metod Sztucznej Inteligencji i Matematyki Stosowanej | ||
| Nauczyciel odpowiedzialny | Marcin Pluciński <Marcin.Plucinski@zut.edu.pl> | ||
| Inni nauczyciele | Marcin Korzeń <Marcin.Korzen@zut.edu.pl>, Wojciech Sałabun <wsalabun@wi.zut.edu.pl> | ||
| ECTS (planowane) | 3,0 | ECTS (formy) | 3,0 |
| Forma zaliczenia | zaliczenie | Język | angielski |
| Blok obieralny | — | Grupa obieralna | — |
Formy dydaktyczne
Wymagania wstępne
| KOD | Wymaganie wstępne |
|---|---|
| W-1 | Knowledge of basics of high mathematics |
Cele przedmiotu
| KOD | Cel modułu/przedmiotu |
|---|---|
| C-1 | Acquirement of competence and practice of knowledge extraction in form of rule basis from information tables about a system of interest. |
Treści programowe z podziałem na formy zajęć
| KOD | Treść programowa | Godziny |
|---|---|---|
| laboratoria | ||
| T-L-1 | Exercises in various methods of attribute discretization. | 2 |
| T-L-2 | Identification of elementary conditional and decisional sets (concepts) from the informational table of a system. Visualization of conditional and decisional sets. Decomposition of decisional sets in conditional ones. | 2 |
| T-L-3 | Determining of absolute and relative attribute reducts, minimal sets of attributes and attribute cores. | 2 |
| T-L-4 | Determining rough models of systems in form of rules' basis. Rules' reduction and verification. | 3 |
| T-L-5 | Calculating quality measures of rough set models, determining of possible risk due to attribute reduction. | 2 |
| T-L-6 | Determining soft rough set models of systems, soft attribute reduction, generating and processing probabilistic rules. | 2 |
| T-L-7 | Constructing the rough set model for a given system as finishing of laboratory exercices. | 2 |
| 15 | ||
| wykłady | ||
| T-W-1 | Example of a real problem solved with use of rough sets, | 1 |
| T-W-2 | Discretization of variables in problems, its meaning and usefulness. Basic ways of discretization. | 1 |
| T-W-3 | Basic notions of rough sets. | 1 |
| T-W-4 | Absolute and relative reduction of redundant system attributes. | 2 |
| T-W-5 | Quality measures of rough set models. | 1 |
| T-W-6 | Generating of certain and uncertain information rules about the system, their reduction and processing. | 2 |
| T-W-7 | Rules' risk occuring due to reduction of conditional attributes. | 1 |
| T-W-8 | "Soft" version of rough sets enabling generating both certain and uncertain (probabilistic) rules and "soft" attribute reduction. | 4 |
| T-W-9 | Example of rough set application showing successive realization steps necessary to correct extraction of rule basis with use of rough sets. | 2 |
| 15 | ||
Obciążenie pracą studenta - formy aktywności
| KOD | Forma aktywności | Godziny |
|---|---|---|
| laboratoria | ||
| A-L-1 | Participation in laboratory excercises | 15 |
| A-L-2 | Consultations referring to laboratory excerces | 10 |
| A-L-3 | Elaborating of the project of an own rough set model of a real system for testing student competence in knowledge extraction from information tables of systems | 35 |
| 60 | ||
| wykłady | ||
| A-W-1 | Participating in consultaions | 3 |
| A-W-2 | Studying of lecture texts and of the recommended literature | 12 |
| A-W-3 | Participation in lectures | 15 |
| 30 | ||
Metody nauczania / narzędzia dydaktyczne
| KOD | Metoda nauczania / narzędzie dydaktyczne |
|---|---|
| M-1 | Information lecture with presentation |
| M-2 | Laboratory excercises in individual solving of sub-problems given by an academician and realization of the end-project summarizing lectures and laboratory |
Sposoby oceny
| KOD | Sposób oceny |
|---|---|
| S-1 | Ocena podsumowująca: Lectures: summarizing evaluation of the student on the basis of the end-project of knowledge extraction with rough sets individually realized by the student with taking into account student activity during lectures. |
| S-2 | Ocena formująca: Laboratory: forming evaluation of the student based on the student activity duaring laboratory training |
Zamierzone efekty uczenia się - wiedza
| Zamierzone efekty uczenia się | Odniesienie do efektów kształcenia dla kierunku studiów | Odniesienie do efektów zdefiniowanych dla obszaru kształcenia | Cel przedmiotu | Treści programowe | Metody nauczania | Sposób oceny |
|---|---|---|---|---|---|---|
| WM-WI_1-_??_W01 The student has knowledge about rough sets, models created on the base of them, and main applications of rough sets. | — | — | C-1 | T-W-1, T-W-2, T-W-3, T-W-4, T-W-5, T-W-6, T-W-7, T-W-8, T-W-9 | M-1 | S-1 |
Zamierzone efekty uczenia się - umiejętności
| Zamierzone efekty uczenia się | Odniesienie do efektów kształcenia dla kierunku studiów | Odniesienie do efektów zdefiniowanych dla obszaru kształcenia | Cel przedmiotu | Treści programowe | Metody nauczania | Sposób oceny |
|---|---|---|---|---|---|---|
| WM-WI_1-_??_U01 The student has the ability to create rough set models in form of rules. | — | — | C-1 | T-L-1, T-L-2, T-L-3, T-L-4, T-L-5, T-L-6, T-L-7 | M-2 | S-2 |
Kryterium oceny - wiedza
| Efekt uczenia się | Ocena | Kryterium oceny |
|---|---|---|
| WM-WI_1-_??_W01 The student has knowledge about rough sets, models created on the base of them, and main applications of rough sets. | 2,0 | |
| 3,0 | The student has the basic knowledge about rough sets and rough set models. | |
| 3,5 | ||
| 4,0 | ||
| 4,5 | ||
| 5,0 |
Kryterium oceny - umiejętności
| Efekt uczenia się | Ocena | Kryterium oceny |
|---|---|---|
| WM-WI_1-_??_U01 The student has the ability to create rough set models in form of rules. | 2,0 | |
| 3,0 | The student has the basic practical ability in creating of rough set models. | |
| 3,5 | ||
| 4,0 | ||
| 4,5 | ||
| 5,0 |
Literatura podstawowa
- Lech Polkowski, Rough sets. Mathematical foundations., Physica-Verlag, A Springer-Verlag Company, Heilderberg, New York, 2002, 1
- W. Pedrycz, A. Skowron, V. Kreinowich (editors), Handbook of granular computing, Wiley, Chichester, England, 2008, 1
Literatura dodatkowa
- S.K. Pal, L. Polkowski, A. Skowron (editors), Rough-Neural Computing. Techniques for Computing with Words, Springer, Berlin Heidelberg, New York, 2004, 1