Saltar al contingut Menu
Mapa
  • Inicio
  • Información
  • Contacto
  • Mapa

MINERÍA DE DATOS II (MD2)

Créditos Dept.
7.5 (6.0 ECTS) CS

Profesores

Responsable:  (-)
Otros:(-)

Objectivos Generales

In this course, students should gain an understanding of the concepts behind data mining, its goals, techniques and applications, with a special focus on the application to massive data sets. These applications are experiencing a rapid growth in astronomy, marketing and genomics, among other disciplines, and demand supercomputing architectures and scalable algorithms. The course will have a practical side, with assignments using different data sets and techniques, such as advanced visualization, statistical machine learning and model optimization.

Objectivos Específicos

Conocimientos

  1. Advanced issues in data mining, such as exploratory visualization, statistical machine learning and kernel methods, and mining large datasets through supercomputing.
  2. Insight from real applications of data mining in industry, including the mining of large datasets through supercomputing.

Habilidades

  1. Ability to identify a problem suitable for data mining.
  2. Identification of the most appropriate technique or techniques for a given problem.
  3. Practical application to large datasets through supercomputing.

Competencias

  1. Be able to design a data mining application for a specific problem.
  2. Be able to work in group to discuss the use of different data mining models and techniques for a given application.

Contenidos

Horas estimadas de:

T P L Alt L Ext. Est O. Ext.
Teoria Problemas Laboratorio Otras actividades Laboratorio externo Estudio Otras horas fuera del horario fijado

1. Introduction to data mining and CRISP-DM 2.0.
T      P      L      Alt    L Ext. Est    O. Ext. Total 
2,0 0 0 0 0 2,0 0 4,0

2. Dimensionality reduction, feature selection and extraction.
T      P      L      Alt    L Ext. Est    O. Ext. Total 
6,0 2,0 0 0 5,0 6,0 0 19,0

3. Visualization in data mining.
T      P      L      Alt    L Ext. Est    O. Ext. Total 
4,0 4,0 4,0 0 10,0 4,0 0 26,0

4. Statistical Machine Learning.
T      P      L      Alt    L Ext. Est    O. Ext. Total 
6,0 4,0 8,0 0 10,0 4,0 0 32,0

5. Case studies in data mining and supercomputing.
T      P      L      Alt    L Ext. Est    O. Ext. Total 
6,0 4,0 10,0 0 15,0 10,0 0 45,0


Total por tipo T      P      L      Alt    L Ext. Est    O. Ext. Total 
24,0 14,0 22,0 0 40,0 26,0 0 126,0
Horas adicionales dedicadas a la evaluación 0
Total horas de trabajo para el estudiante 126,0

Metodología docente

Classes building up theoretical and methodological concepts in a structured fashion. Problem-oriented classes focusing on a set problem assignments. Laboratory classes focusing on co-operative work and practical applications in order to consolidate concepts, skills and competencies.

Método de evaluación

The course will be evaluated through a final project and its corresponding written report and oral presentation.

Bibliografía básica

  • Hand, D., Manila, H., Smyth, P. Principles of Data Mining, The MIT Press, 2001.
  • U.Fayyad et al. (Eds.) Information Visualization in Data Mining and Knowledge Discovery, Morgan Kaufmann, 2001.
  • Guo, Y., Grossman, R. High Performance Data Mining: Scaling Algorithms, Applications and Systems, Kluwer, 2000.

Bibliografía complementaria

  • Bishop, C.M. Pattern Recognition and Machine Learning, Springer Verlag, 2006.
  • MacKay, David Information Theory, Inference & Learning Algorithms, Cambridge Univ. Press, 2002.

Enlaces web

  1. http://www.kdnuggets.com


  2. http://www.kernel-machines.org/


Capacidades previas

Basic understanding of multivariate data analysis and machine learning techniques.


Compartir
Share

 
logo FIB © Facultad de Informática de Barcelona - Contacto - RSS
Esta web utiliza cookies propias para ofrecerle una mejor experiencia y servicio. Si continúa la navegación, entendemos que acepta nuestra política de cookies. Versión clássica Versión móvil