Systemy wspomagania decyzji Field of study: Computer Science
Programme code: W4-N2IN19.2021

Module name: Systemy wspomagania decyzji
Module code: W4-IN-N2-20-F-SWD
Programme code: W4-N2IN19.2021
Semester:
  • winter semester 2023/2024
  • summer semester 2022/2023
  • winter semester 2022/2023
  • summer semester 2021/2022
Language of instruction: Polish
Form of verification: course work
ECTS credits: 4
Description:
The aim of the course is to prepare students for the design and implementation of decision support systems. In addition to the theoretical foundations, the student gains the ability to implement practical systems supporting decisions in the fields of banking, commerce and other.
Prerequisites:
(no information given)
Key reading:
1. Robert T. Clement, Making Hard Decisions: An Introduction to Decision Analysis, Second Edition. Duxbury Press, 1996. 2.Przemysław Grzegorzewski, Wspomaganie decyzji w warunkach niepewnośc. Metody statystyczne dla nieprecyzyjnych danych. EXIT, Warszawa, 2006 3. S.Russel, P. Norvig:Artificial Intelligence. A Modern Approach, ISBN-13: 978-0136042594
Learning outcome of the module Codes of the learning outcomes of the programme to which the learning outcome of the module is related [level of competence: scale 1-5]
The student has basic knowledge of decision support systems. [M_001]
 K_W02 [1/5] K_W09 [1/5] K_U01 [1/5] K_U05 [1/5] K_U09 [1/5] K_U10 [1/5] K_K04 [1/5]
The student has basic knowledge in the field of utility theory, the application of deterministic (Hurwicz, Laplace) and non-deterministic criteria (e.g. max. Expected utility) in decision support systems. [M_002]
 K_W02 [1/5] K_W05 [1/5] K_U01 [1/5] K_U05 [1/5] K_U09 [1/5]
The student has basic knowledge of Bayesian networks and their applications in supported decisions. [M_003]
 K_W01 [1/5] K_U05 [1/5]
The student has basic knowledge of time series prediction as part of the decision support system. [M_004]
 K_W02 [1/5] K_U01 [1/5]
The student is able to construct decision support systems on the Genie platform based on ordinary and dynamic Bayesian networks, can implement the Java decision support system using the SMILE library. [M_005]
 K_U01 [1/5] K_U05 [1/5] K_U08 [1/5] K_U09 [1/5] K_U10 [1/5]
he student is able to construct complex decision support systems implemented using the KNIME package, including time series prediction. [M_006]
 K_U01 [1/5] K_U05 [1/5] K_U08 [1/5] K_U09 [1/5] K_U10 [1/5]
Type Description Codes of the learning outcomes of the module to which assessment is related
Solving decision problems. [W_001]
Solution of three theoretical tasks, also of a computational nature.
 M_001 M_002 M_003
Design and implementation of a decision support system. [W_002]
Implementation of the decision support system using the selected platform: 1) Genie / SMILE 2) KNIME
 M_004 M_005 M_006
Form of teaching Student's own work Assessment of the learning outcomes
Type Description (including teaching methods) Number of hours Description Number of hours
lecture [Z_001]
Lecture in form of the slides presentation.
 15
Study of lecture notes, compulsory and supplementary literature.
 15 Solving decision problems. [W_001] Design and implementation of a decision support system. [W_002]
laboratory classes [Z_002]
During classes, the lecturer presents and discusses examples of decision support systems implemented in Genie, QGenie and KNIME. Students independently develop the systems indicated by the teacher. students implement two decision support systems on the Genie/SMILE and KNIME toolkits.
 30
Students implement two decision support systems based on the GENIE / SMILE and KNIME toolkits.
 60 Solving decision problems. [W_001] Design and implementation of a decision support system. [W_002]
Attachments
Module description (PDF)
Information concerning module syllabuses might be changed during studies.
Syllabuses (USOSweb)
Semester Module Language of instruction
(no information given)