Statistical techniques in risk analysis and quality control.

Fundamentals and techniques of Business Intelligence.

Decision support systems.

Modeling of economic-business systems. Financial derivatives: Risk management.

Applications (E-commerce, Decision support environment, Instruments on interest rates, risk, and valuation).

Decision problems: Process and environment of a decision problem. Components, symbolic representation, classification.

Decision support systems: Preference systems. Representation systems. Concept and architecture. Classification. Evaluation of DSS systems.

Deterministic utility theories and expected utility: Existence and uniqueness of value functions. Decomposition rules. Bernoulli's principle. Mixture spaces. Construction and decomposition of expected utility functions.

Allocation methodologies: Decision trees and influence diagrams. Direct assignment of value/utility functions. Methods based on sequential elimination. Methods based on indifference curves. Construction of a decision tree. Resolution of a decision tree. Value of information. Definition of an influence diagram. Specific cases. Inference in an influence diagram. Construction and machine learning of decision support systems.

Business Intelligence, fundamentals, and techniques: Concepts and mathematical foundation. Data warehousing (repository). Data Mining and online transaction processing (OLTP). Business Intelligence: Systems and software. Quality control in production and service processes.

Risk management: Modeling in the financial world. Derivatives (options, futures, and swaps). Financial risk and its measurement. Market valuation. Instruments on interest rates, risk, and valuation. Acceptance sampling and reliability of systems.

Analysis of medical decisions: Application of Bayesian networks and influence diagrams to medical decisions.

A more detailed teaching guide can be achieved through the Moodle platform.

1. Written exam on the first 2 topics. 3.5 points out of the total. Midway through the course.



2. Written exam on the last 2 topics. 3.5 points out of the total. At the end of the course.



3. Laboratory practices. 3 points out of the total. Throughout the course.



If the student obtains half of the points assigned to the first exam, the grade will be saved to be added to the rest of the scores. If not, the student must take the second exam, which will then be worth 7 points and will cover all topics. Therefore, the first exam serves to cover part of the syllabus.



The final grade will be the sum of the 3 assessments. The course is passed by achieving a final grade of 5, with a minimum of 3.5 points in the sum of the 2 exam grades.

Notes will be available through the Moodle platform.

Basic bibliography

S. Ríos, C. Bielza, A. Mateos. Fundamentos de los sistemas de ayuda a la decisión. Ra-Ma, 2002.

O.G. León. Tomar decisiones difíciles. McGraw Hill, 2000

S. Holtzman. Intelligent decision systems. Addison Wesley, 1989.

Business intelligence: Técnicas de análisis para la toma de decisiones. Elizabeth Vitt, Michael Luckevich, Stacia Misner. McGraw-Hill 2003.

Francisco Javier Díez Vegas, Teoría probabilista de la decisión en medicina. Informe Técnico CISIAD-07-01 UNED, Madrid 2007.

Francisco Javier, Díez, Introducción a los modelos gráficos probabilistas, Departamento de Inteligencia Artificial, Uned, Octubre de 2007

In-depth bibliography

- Alex Berson and Stephen J. Smith. Data Warehousing, Data Mining & OLAP. McGraw-Hill, 2001
- M.S. Silver. Systems that support decision makers: description and analysis. Wiley, 1991.
- George Marakas. Decision Support Systems. Prentice Hall, 2001.
- Sistemas Expertos y Modelos de Redes Probabilisticas, Enrique Castillo y otros, Universidad de Cantabria.

Journals

Decission Support Systems
Elsevier