Decision Support Systems 1000-135SYD
Basic notions on logics, statistics and algorithmics. Concept approximation, discernibility between objects, rough sets. Positive regions, attribute dependency, decision rules. Entropy and conditional entropy.
Different approaches to the description of vague concepts.
Boolean functions and Approximate Boolean reasoning in the calculation of reducts for information systems and decision systems. Decision and association rules, discretisation and symbolic value grouping. Computational complexity and heuristics.
Classifiers and their construction methods. Quality evaluation of classifiers in inductive reasoning. Minimal description length principle, applications.
Computational Learning Theory. PAC algorithms. VC dimention. Learnability of a hypothesis space. Minimal number of objects for learning.
Laboratory classes with data analysis toolkits like RSES, WEKA, R, etc.
Type of course
foreign languages
Mode
Self-reading
Prerequisites (description)
Course coordinators
Learning outcomes
Students should be familiar with the basic artificial intelligence techniques and their application in decision support systems, in particular the methods for creating classifiers from data, and the evaluation techniques for those classifiers.
Assessment criteria
2 practical assignments (50%) and exam (50%).
The student can apply for the exam earlier if he/she will receive at least 80% of marks for both assignments 2 weeks before the end of the semester.
Bibliography
Intelligent Decision Support Systems Applications in Signal Processing. Ed. by Borra, Surekha / Dey, Nilanjan / Bhattacharyya, Siddhartha / Bouhlel, Mohamed Salim, DeGruyter, 2019.
Power, D.J. A Brief History of Decision Support Systems, DSSResources.COM, World Wide Web, version 4.0, March 10, 2007
M. Anthony, N. Biggs, Computational learning theory. Cambridge University Press, Cambridge 1992
E.M. Brown. Boolean Reasoning. Kluwer Acad. Publ., Dordrecht 1990
R.O. Duda, P.E. Hart, D. Stork. Pattern classification. John Wiley and Sons, 2001
S. Russell, P. Norvig. AI: Modern approach. Prentice Hall, Englewood Cliffs, New Jersey, 2003
L. Polkowski, A. Skowron (eds.) Rough Sets in Knowledge Discovery. Physica-Verlag, Heidelberg, 1998.
Additional information
Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:
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