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Intelligent Decision Support Systems
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Intelligent Decision Support Systems
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Academic year 2014/2015
- Course ID
- IDSS2014
- Teaching staff
- Prof. Stefania Montani (Titolare del corso)
Prof. Luigi Portinale (Titolare del corso) - Year
- 2° anno
- Type
- A scelta dello studente
- Credits/Recognition
- 2
- Course disciplinary sector (SSD)
- INF/01 - informatica
- Delivery
- Tradizionale
- Language
- Italiano
- Attendance
- Facoltativa
- Type of examination
- Orale
- Prerequisites
- An overview of basic AI methodologies
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Sommario del corso
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Course objectives
The course aims at introducing Artificial Intelligence (AI) methodologies for the development of Intelligent Decision Support Systems (IDSS).
A general introduction to the topic of intelligent decision support will be provided, followed by the presentation and discussion of two main methodologies: Case-Based Reasoning (CBR) and Probabilistic Graphical Models (PGM) like Bayesian Networks and Influence Diagrams.
Examples in the areas of Business Intelligence, Planning under Uncertainty and Reliability of Systems will be provided.
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Results of learning outcomes
General overview knowledge about CBR and PGM, and proof of an in-depth analysis of one specific topic.
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Learning assessment methods
Oral examination
Oral examination - seminar
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Program
- Introduction to intelligent decision support (S. Montani)
- Case-Based Reasoning: (S. Montani)
- Fundamentals,
- Case Representation,
- Case Retrieval,
- Classification,
- Advanced Techniques (CBR for time-series management, fuzzy-CBR)
- Bayesian Networks: (L. Portinale)
- Fundamentals,
- Modeling Issues,
- Inference Algorithms,
- Sensitivity Analysis
- Decision Theory (outline) (L. Portinale)
- Influence Diagrams: (L. Portinale)
- Modeling issues,
- Inference Techniques
Suggested readings and bibliography
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Material provided by teachers
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