Análisis y Minería de Datos Inteligente

• Profesorado
• Horas semanales
• Competencias
• Objetivos
• Contenidos
• Actividades
• Metodología docente
• Método de evaluación
• Bibliografía
• Capacidades previas

Profesorado

Responsable

• Alfredo Vellido Alcacena ( )

Otros

• Carlos Cano Domingo ( )
• Caroline König ( )

Horas semanales

Competencias

Competencias Técnicas Genéricas

Genéricas

• CG3 - Capacidad para la modelización, cálculo, simulación, desarrollo e implantación en centros tecnológicos y de ingeniería de empresa, particularmente en tareas de investigación, desarrollo e innovación en todos los ámbitos relacionados con la Inteligencia Artificial.

Competencias Técnicas de cada especialidad

Académicas

• CEA4 - Capacidad de comprender los principios básicos de funcionamiento de las técnicas principales de Inteligencia Computacional, y saber utilizarlas en el entorno de un sistema o servicio inteligente.
• CEA7 - Capacidad de comprender la problemática, y las soluciones a los problemas en la práctica profesional de la aplicación de la Inteligencia Artificial en el entorno empresarial e industrial.
• CEA11 - Capacidad de comprender las técnicas avanzadas de Inteligencia Computacional, y saber diseñar, implementar y aplicar estas técnicas en el desarrollo de aplicaciones, servicios o sistemas inteligentes.

Profesionales

• CEP1 - Capacidad de resolver las necesidades de analisis de la informacion de las diferentes organizaciones, identificando las fuentes de incertidumbre y variabilidad.
• CEP5 - Capacidad de diseñar nuevas herramientas informáticas y nuevas técnicas de Inteligencia Artificial en el ejercicio profesional.

Competencias Transversales

Uso solvente de los recursos de información

• CT4 - Gestionar la adquisicion, la estructuracion, el analisis y la visualizacion de datos e informacion en el ambito de la especialidad y valorar de forma critica los resultados de esta gestion.

Razonamiento

• CT6 - Capacidad de evaluar y analizar de manera razonada y critica sobre situaciones, proyectos, propuestas, informes y estudios de caracter cientifico-tecnico. Capacidad de argumentar las razones que explican o justifican tales situaciones, propuestas, etc.

Analisis y sintesis

• CT7 - Capacidad de analisis y resolucion de problemas tecnicos complejos.

Objetivos

1. Presenting DM as a process that should involve a methodology id applied at its best.
   Competencias relacionadas: CEA7, CEP5, CT4, CT6,
2. Introducing the students to the new concept of DM for processes, called Process Mining.
   Competencias relacionadas: CEA7, CG3, CEP1, CEP5, CT4, CT6,
3. Delving into some detail in one of the stages of DM: data exploration
   Competencias relacionadas: CEA4, CG3, CEP1, CT4,
4. Dealing in detail with the problem of data visualization for exploration as a key issue in DM.
   Competencias relacionadas: CEA11, CG3, CEP1, CEP5, CT4, CT6,
5. Introducing the students to the basics of probability theory as applied in Intelligent Data Analysis (IDA)
   Competencias relacionadas: CEP1, CT4, CT6, CT7,
6. Introducing the students to the probabilistic variant of IDA in the form of Statistical Machine Learning, both for supervised and unsupervised learning models.
   Competencias relacionadas: CEA11, CG3, CT4, CT6, CT7,
7. Dealing in detail with different unsupervised models for data visualization, including case studies.
   Competencias relacionadas: CEA11, CG3, CEP1, CEP5, CT4, CT6, CT7,
8. Approaching the multi-faceted concept of data mining (DM) from different perspectives.dies.
   Competencias relacionadas: CEA7, CG3, CEP5, CT4, CT6, CT7,

Contenidos

1. Introduction to the concept of data mining (DM).
   DM is a multi-faceted concept that requires discussion and clarification. We will do this at the beginning of the course.
2. DM as a methodology.
   We argue that DM should not be focused on the concept of data analysis/modeling, but, instead, should be treated as a methodology with diverse inter-related stages.
3. DM for processes: Process Mining.
   A new development in DM methodologies is that which deals with one specifically suited for processes. It is called Process Mining and will be described and discussed in this course.
4. Data exploration in DM.
   One of the main stages of well-structures DM methodologies is Data exploration. It will be discussed as a preamble to data visualization.
5. Basics of probability theory in Intelligent Data Analysis (IDA)
   For a long time in the last half-century, multivariate statistics and artificial intelligence (mostly in the field of machine learning) have developed in parallel without fully meeting. Statistical machine learning has bridged that field over the last two decades. We introduce it by first providing some basic principles of probability theory (Bayesian inference).
6. Data visualization for exploration.
   One of the aspects of the problem of data exploration is data visualization. It has a research 'life' of its own as it involves not only computer-based mathematical models, but also natural perception and processing.
7. Statistical Machine Learning for IDA: supervised models.
   Once the basics of Bayesian inference are set, we will delve into the field of Statistical Machine Learning for IDA, starting with supervised learning models, with an emphasis on feed-forward artificial neural networks.
8. Statistical Machine Learning for IDA: unsupervised models.
   Once the basics of Bayesian inference and of Statistical Machine Learning for IDA in supervised models are set, we will continue with unsupervised models, focusing on self-organizing maps and related models.
9. Unsupervised models for data visualization, with case studies.
   In the final item of the contents of the course, we will bring statistical machine learning and data visualization together by discussing some probabilistic unsupervised learning models for data visualization, including some case studies as an example.

Actividades

Actividad Acto evaluativo

Metodología docente

Método de evaluación

Bibliografía

Básica:

• Information theory, inference, and learning algorithms - MacKay, D.J.C, Cambridge University Press, 2003. ISBN: 0521642981
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991002876809706711&context=L&vid=34CSUC_UPC:VU1&lang=ca
• Principles of data mining - Hand, D.; Mannila, H.; Smyth, P, MIT Press, 2001. ISBN: 026208290X
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991002287109706711&context=L&vid=34CSUC_UPC:VU1&lang=ca
• Pattern recognition and machine learning - Bishop, C.M, Springer, 2006. ISBN: 0387310738
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991003157379706711&context=L&vid=34CSUC_UPC:VU1&lang=ca

Complementaria:

• Statistics: a very short introduction - Hand, D.J, Oxford University Press , 2008. ISBN: 9780199233564
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991003868839706711&context=L&vid=34CSUC_UPC:VU1&lang=ca
• Information visualization: an introduction - Spence, R, Springer , 2020. ISBN: 9783319073408
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991004193539706711&context=L&vid=34CSUC_UPC:VU1&lang=ca
• Visualize this: the flowing data guide to design, visualization, and statistics - Yau, N, Wiley , 2011. ISBN: 9780470944882
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991003948649706711&context=L&vid=34CSUC_UPC:VU1&lang=ca

Capacidades previas