1. SylabUZ
2. Faculty of Computer Science, Electrical Engineering and Automatics
3. winter term 2018/2019
4. WIEiA - oferta ERASMUS / Automatic Control and Robotics - Second-cycle Erasmus programme
5. Advanced decision support systems

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Advanced decision support systems - course description

Aim of the course

• to familiarize students with advanced techniques of extracting knowledge from data
• to know methods of applying soft computing in decision making systems
• shaping the skills of building hybrid expert systems
• acquiring skills in building decision systems with uncertain and imprecise knowledge

Prerequisites

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Decision support systems, Artificial intelligence methods.

Scope

Making decisions in the conditions of incomplete, uncertain and imprecise information. Parametric and nonparametric decision problems. Application of expert systems. Theory of possibilities. Application of rough and fuzzy sets in knowledge bases. Decision tree optimization. Discovering knowledge in databases, data mining. Preliminary preparation of data. The use of soft calculations in extracting knowledge from data (data mining).

Application of neural networks in decision making. Neural networks in grouping and classification. Extraction of knowledge from data using neural networks.
Fuzzy decision systems. Neurofuzzy systems in creating knowledge base. Fuzzy classifiers. Various types of neuro-fuzzy decision-making systems.
The use of rough sets in decision support. Rough sets based on dominance. Induction of classification patterns in the form of decision rules.
Designing decision support systems. Hybrid decision systems.

Teaching methods

Lecture, laboratory exercises

Learning outcomes and methods of theirs verification

Outcome description	Outcome symbols	Methods of verification	The class form

Assignment conditions

Lecture – the main condition to get a pass is a sufficient mark in a written or oral exam.
Laboratory – the passing condition is to obtain positive marks from all laboratory exercises to be planned during the semester.
Calculation of the final grade: lecture 50% + laboratory 50%

Recommended reading

J. Łęski, Systemy neuronowo-rozmyte, Wydawnictwa Naukowo-Techniczne, Warszawa, 2008.
2. R. K. Nowicki, Rozmyte systemy decyzyjne w zadaniach z ograniczoną wiedzą, Akademicka Oficyna Wydawnicza Exit, Warszawa, 2009.
3. D. Rutkowska, M. Piliński, L. Rutkowski, Sieci neuronowe, algorytmy genetyczne i zbiory rozmyte, Wydawnictwo Naukowe PWN, Warszawa, 1999.
4. J. Surma J.: Business Intelligence Systemy wspomagania decyzji biznesowych, WN PWN SA, Warszawa 2012.
5. D.T. Laros: Metody i modele eksploracji danych. WN PWN SA, Warszawa 2012.

Further reading

1. Pieczyński, Reprezentacja wiedzy w diagnostycznym systemie ekspertowym, Lubuskie Towarzystwo Naukowe w Zielonej Górze, Zielona Góra, 2003.
2. B. Nadiru, J. Y. Cheung, Fuzzy Engineering Expert Systems with Neural Network Applications, John Wiley & Sons, Inc. New York, 2002.

Notes

Modified by dr hab. inż. Wojciech Paszke, prof. UZ (last modification: 02-05-2020 14:43)