Induced Decision Rules
Informacje ogólne
Kod przedmiotu: | 132461-S |
Kod Erasmus / ISCED: |
11.9
|
Nazwa przedmiotu: | Induced Decision Rules |
Jednostka: | Szkoła Główna Handlowa w Warszawie |
Grupy: |
Przedmioty kierunkowe do wyboru NLLS-MIS |
Punkty ECTS i inne: |
6.00 (zmienne w czasie)
|
Język prowadzenia: | angielski |
Efekty uczenia się: |
Wiedza: 1. Classical rule generation algorithms (naive bayes, decision trees, decision rules). 2. Areas of application of classification rules in decision support in business practice. 3. Method of assessment of predictive power of classification models. Umiejętności: 1. Install and operate R software. 2. Construct classification models by herself, verify their quality and create forecasts using them. 3. Use large databases for building of classification models. 4. Apply classification models for decision support in business practice. Kompetencje społeczne: 1. Communication in polish of results of conducted data analysis for experts and. decision makers 2. Ability to self-study and knowledge actualization in the area of data-mining. |
Zajęcia w cyklu "Preferencje - Semestr letni 2024/25" (jeszcze nie rozpoczęty)
Okres: | 2025-02-15 - 2025-09-30 |
Przejdź do planu
PN WT ŚR CZ PT |
Typ zajęć: |
Zajęcia prowadzącego
|
|
Koordynatorzy: | (brak danych) | |
Prowadzący grup: | Małgorzata Wrzosek | |
Lista studentów: | (nie masz dostępu) | |
Zaliczenie: |
Przedmiot -
Ocena
Zajęcia prowadzącego - Ocena |
Zajęcia w cyklu "Semestr letni 2024/25" (jeszcze nie rozpoczęty)
Okres: | 2025-02-15 - 2025-09-30 |
Przejdź do planu
PN WT ŚR CZ PT |
Typ zajęć: |
Laboratorium, 6 godzin
Wykład, 8 godzin
|
|
Koordynatorzy: | (brak danych) | |
Prowadzący grup: | (brak danych) | |
Lista studentów: | (nie masz dostępu) | |
Zaliczenie: |
Przedmiot -
Ocena
Wykład - Ocena |
|
Skrócony opis: |
Methods of creation the decision rules based on the data analysis. Algorithms of rules induction, verification techniques for the created model robustness and the applicability of the rules for the decision support. The methods are discussed both from theoretical and from business perspective. |
|
Pełny opis: |
1. Present concepts, reasoning and results connected with practical usage of decision rules. 2. Show computer algorithms and software used for generation of decision rules from large databases. 3. Build the ability to generate rules and interpret, verify and select them. 4. Teach how to identify decision situations in business practice in which generation of decision rules would give benefit. The course objective is to teach students the methods of decision rules identification based on analysis of data. Students learn basic algorithms generating such rules. During the course the stress is put on verification of robustness of the created model and its application in decision support. The methods are introduced from theoretical perspective and with focus on their application in business practice. |
|
Literatura: |
Literatura podstawowa: (1) Witten Ian H., Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques (Second Edition), Morgan Kaufmann, 2005. (2) Hongbo Du (2010) Data Mining - techniques and application, Cengage. Literatura uzupełniająca: (3) Hand David, Manilla Heikki, Smyth Padhraic: Principles of Data Mining. MIT Press 2001 |
|
Uwagi: |
Kryteria oceniania: kolokwium: 42.00% ocena z ćwiczeń: 42.00% projekty: 16.00% |
Zajęcia w cyklu "Semestr zimowy 2024/25" (w trakcie)
Okres: | 2024-10-01 - 2025-02-14 |
Przejdź do planu
PN WT ŚR CZ PT |
Typ zajęć: |
Laboratorium, 6 godzin
Wykład, 8 godzin
|
|
Koordynatorzy: | (brak danych) | |
Prowadzący grup: | (brak danych) | |
Lista studentów: | (nie masz dostępu) | |
Zaliczenie: |
Przedmiot -
Ocena
Wykład - Ocena |
|
Skrócony opis: |
Methods of creation the decision rules based on the data analysis. Algorithms of rules induction, verification techniques for the created model robustness and the applicability of the rules for the decision support. The methods are discussed both from theoretical and from business perspective. |
|
Pełny opis: |
1. Present concepts, reasoning and results connected with practical usage of decision rules. 2. Show computer algorithms and software used for generation of decision rules from large databases. 3. Build the ability to generate rules and interpret, verify and select them. 4. Teach how to identify decision situations in business practice in which generation of decision rules would give benefit. The course objective is to teach students the methods of decision rules identification based on analysis of data. Students learn basic algorithms generating such rules. During the course the stress is put on verification of robustness of the created model and its application in decision support. The methods are introduced from theoretical perspective and with focus on their application in business practice. |
|
Literatura: |
Literatura podstawowa: (1) Witten Ian H., Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques (Second Edition), Morgan Kaufmann, 2005. (2) Hongbo Du (2010) Data Mining - techniques and application, Cengage. Literatura uzupełniająca: (3) Hand David, Manilla Heikki, Smyth Padhraic: Principles of Data Mining. MIT Press 2001 |
|
Uwagi: |
Kryteria oceniania: kolokwium: 42.00% ocena z ćwiczeń: 42.00% projekty: 16.00% |
Zajęcia w cyklu "Semestr letni 2023/24" (zakończony)
Okres: | 2024-02-24 - 2024-09-30 |
Przejdź do planu
PN WT ŚR CZ PT |
Typ zajęć: |
Laboratorium, 6 godzin
Wykład, 8 godzin
|
|
Koordynatorzy: | (brak danych) | |
Prowadzący grup: | (brak danych) | |
Lista studentów: | (nie masz dostępu) | |
Zaliczenie: |
Przedmiot -
Ocena
Wykład - Ocena |
|
Skrócony opis: |
Methods of creation the decision rules based on the data analysis. Algorithms of rules induction, verification techniques for the created model robustness and the applicability of the rules for the decision support. The methods are discussed both from theoretical and from business perspective. |
|
Pełny opis: |
1. Present concepts, reasoning and results connected with practical usage of decision rules. 2. Show computer algorithms and software used for generation of decision rules from large databases. 3. Build the ability to generate rules and interpret, verify and select them. 4. Teach how to identify decision situations in business practice in which generation of decision rules would give benefit. The course objective is to teach students the methods of decision rules identification based on analysis of data. Students learn basic algorithms generating such rules. During the course the stress is put on verification of robustness of the created model and its application in decision support. The methods are introduced from theoretical perspective and with focus on their application in business practice. |
|
Literatura: |
Literatura podstawowa: (1) Witten Ian H., Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques (Second Edition), Morgan Kaufmann, 2005. (2) Hongbo Du (2010) Data Mining - techniques and application, Cengage. Literatura uzupełniająca: (3) Hand David, Manilla Heikki, Smyth Padhraic: Principles of Data Mining. MIT Press 2001 |
|
Uwagi: |
Kryteria oceniania: kolokwium: 42.00% ocena z ćwiczeń: 42.00% projekty: 16.00% |
Zajęcia w cyklu "Semestr zimowy 2023/24" (zakończony)
Okres: | 2023-10-01 - 2024-02-23 |
Przejdź do planu
PN WT ŚR CZ PT |
Typ zajęć: |
Laboratorium, 6 godzin
Wykład, 8 godzin
|
|
Koordynatorzy: | (brak danych) | |
Prowadzący grup: | (brak danych) | |
Lista studentów: | (nie masz dostępu) | |
Zaliczenie: |
Przedmiot -
Ocena
Wykład - Ocena |
|
Skrócony opis: |
Methods of creation the decision rules based on the data analysis. Algorithms of rules induction, verification techniques for the created model robustness and the applicability of the rules for the decision support. The methods are discussed both from theoretical and from business perspective. |
|
Pełny opis: |
1. Present concepts, reasoning and results connected with practical usage of decision rules. 2. Show computer algorithms and software used for generation of decision rules from large databases. 3. Build the ability to generate rules and interpret, verify and select them. 4. Teach how to identify decision situations in business practice in which generation of decision rules would give benefit. The course objective is to teach students the methods of decision rules identification based on analysis of data. Students learn basic algorithms generating such rules. During the course the stress is put on verification of robustness of the created model and its application in decision support. The methods are introduced from theoretical perspective and with focus on their application in business practice. |
|
Literatura: |
Literatura podstawowa: (1) Witten Ian H., Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques (Second Edition), Morgan Kaufmann, 2005. (2) Hongbo Du (2010) Data Mining - techniques and application, Cengage. Literatura uzupełniająca: (3) Hand David, Manilla Heikki, Smyth Padhraic: Principles of Data Mining. MIT Press 2001 |
|
Uwagi: |
Kryteria oceniania: kolokwium: 42.00% ocena z ćwiczeń: 42.00% projekty: 16.00% |
Właścicielem praw autorskich jest Szkoła Główna Handlowa w Warszawie.