Administracja Centralna Uczelni - Wymiana międzynarodowa (S2)
Sylabus przedmiotu Data Mining Algorithms:
Informacje podstawowe
| Kierunek studiów | Wymiana międzynarodowa | ||
|---|---|---|---|
| Forma studiów | studia stacjonarne | Poziom | drugiego stopnia |
| Tytuł zawodowy absolwenta | |||
| Obszary studiów | — | ||
| Profil | |||
| Moduł | — | ||
| Przedmiot | Data Mining Algorithms | ||
| Specjalność | przedmiot wspólny | ||
| Jednostka prowadząca | Katedra Metod Sztucznej Inteligencji i Matematyki Stosowanej | ||
| Nauczyciel odpowiedzialny | Przemysław Klęsk <pklesk@wi.zut.edu.pl> | ||
| Inni nauczyciele | |||
| ECTS (planowane) | 4,0 | ECTS (formy) | 4,0 |
| Forma zaliczenia | zaliczenie | Język | angielski |
| Blok obieralny | — | Grupa obieralna | — |
Formy dydaktyczne
Wymagania wstępne
| KOD | Wymaganie wstępne |
|---|---|
| W-1 | mathematics |
| W-2 | programming |
| W-3 | algorithms and data structures |
Cele przedmiotu
| KOD | Cel modułu/przedmiotu |
|---|---|
| C-1 | Building the understanding about learning from data. |
| C-2 | Familiarization with probabilistic, tree-based, and boosted classifiers, and the related algorithms. |
| C-3 | Familiarization with rules mining and related algorithms. |
Treści programowe z podziałem na formy zajęć
| KOD | Treść programowa | Godziny |
|---|---|---|
| laboratoria | ||
| T-L-1 | Programming the naive Bayes classifier (MATLAB) - for 'wine data set' (in class) and a selected data set (homework). | 8 |
| T-L-2 | Programming the Apriori algorithm - mining association rules. | 6 |
| T-L-3 | Programming an exhaustive generator of decision rules (for given premise length). | 6 |
| T-L-4 | Programming the CART algorithm - building a complete tree. | 4 |
| T-L-5 | Programming heuristics for pruning CART trees. | 6 |
| 30 | ||
| wykłady | ||
| T-W-1 | Review of some elements of probability calculus. Derivation of Naive Bayes classifier. Remarks on computational complexity with and without the naive assumption. Bayes rule. LaPlace correction. Beta distributions. | 4 |
| T-W-2 | Mining association rules by means of Apriori algorithm. Support and confidence measures. Finding frequent sets (induction). Rules generation mechanics. Remarks on the hashmap data structure applied for Apriori algorithm. Pareto-optimal rules. Remarks on decision rules generation. | 4 |
| T-W-3 | Decision trees and CART algorithm. Impurity functions and their properties. Best splits as minimizers of expected impurity of children nodes. CART greedy algorithm. Tree pruning heuristics (by depth, by penalizing number of leafs). Recursions for traversing the subtrees (greedy and exhaustive). | 3 |
| T-W-4 | Ensemble methods: bagging and boosting (meta classifiers). AdaBoost algorithm. Exponential criterion vs zero-one-loss function. Real boost algorithm. | 2 |
| T-W-5 | Exam. | 2 |
| 15 | ||
Obciążenie pracą studenta - formy aktywności
| KOD | Forma aktywności | Godziny |
|---|---|---|
| laboratoria | ||
| A-L-1 | Participation in lab classes. | 30 |
| A-L-2 | Programming homework tasks. | 24 |
| A-L-3 | Preparation for short tests (15 min) carried out in lab classes. | 6 |
| 60 | ||
| wykłady | ||
| A-W-1 | Participation in lectures. | 13 |
| A-W-2 | Sitting for the exam. | 2 |
| A-W-3 | Preparation for the exam. | 16 |
| 31 | ||
Metody nauczania / narzędzia dydaktyczne
| KOD | Metoda nauczania / narzędzie dydaktyczne |
|---|---|
| M-1 | Lectures. |
| M-2 | Computer programming. |
Sposoby oceny
| KOD | Sposób oceny |
|---|---|
| S-1 | Ocena formująca: Four short tests (15 minutes long) at the end of each topic during the lab. |
| S-2 | Ocena formująca: Four grades for the programs written as homeworks. |
| S-3 | Ocena podsumowująca: Final grade for the lab calculated as a weighted mean from partial grades: - tests (weight: 40%), - programs (weight: 60%). |
| S-4 | Ocena podsumowująca: Final grade for lectures from the test (2 h). |
Literatura podstawowa
- M. J. Zaki, W. Meira Jr, "Data Mining and Analysis - Fundamental Concepts and Algorithms", Cambridge University Press, 2014
- P. Klęsk, Electronic materials for the course available at: http://wikizmsi.zut.edu.pl, 2015