a. Describe course objectives / outcomes and competences (knowledge & skills):
Understanding the basic theory for simple and sequential decisions by an intelligent agent, familiarization with the types of decision support systems, understanding the basic principles of game theory, understanding the theory of decision making with data analysis, understanding data mining and its use in decision making, understanding the developing decision support systems, familiarization with the use of decision support software.
Training on developing decision support systems, training on the use of decision support tools.
Course Content (Syllabus)
Business Intelligence and Decission Support Systems, Decision Making under Certainty(Multi-criteria Decision Making Methods), Decision Making under Ignorance, Decision Making under Risk (Probability Theory, Bayesian Networks, Utility Theory), Sequential Decisions (Decision Trees, Decision Making in the presence of Competitive Agents (Game Theory), Decision Making with Data Analysis (Machine Learning, Knowledge Discovery in Databases, Business Intelligence Software Applications.
Additional bibliography for study
- Data science for Business, Foster Provost & Tom Fawcett, O'Reilly Media, 2013.
- Making Hard Decisions: An Introduction to Decision Analysis, 2nd Edition, Robert T. Clemen, Duxbury Press, 1996.
- Essentials of Management Information Systems, 4th Edition, J. Laudon, Prentice Hall, 2001.
- Τεχνητή Νοημοσύνη, Γ' Έκδοση, Ι.Βλαχάβας, Π.Κεφαλάς, Ν. Βασιλειάδης, Φ.Κόκκορας και Η. Σακελλαρίου. Εκδόσεις Β.Γκιούρδας, 2006.
- Decision support systems: concepts and resources for managers, Power, D. J., Westport, Conn., Quorum Books, 2002.
- Data Science for Business, Foster Provost and Tom Fawcett, O'Reilly Media, 2013
- Practical Data Science With R, Nina Zumel and John Mount, Manning Publications, 2014