Tècniques Avançades d'Aprenentatge Automàtic

• Hores setmanals
• Objectius
• Continguts
• Metodologia docent
• Mètode d'avaluació

Hores setmanals

Objectius

1. Coneixer i fer servir tecniques avançades d'aprenentatge no supervisat i mineria de dades no supervisada per a aplicacions en tots els dominis d'aplicacion de la ingenieria i la ciencia
   Competències relacionades: CB7, CT2, CT4, CEA12, CEA13, CEP1, CG1, CG3,

Continguts

1. Mineria de dades una perspectiva global
   Brief introduction to what is Data Mining and Knowledge Discovery, the areas they are related to and the different techniques involved
2. Preprocessament/Transformacio no supervisada de dades
   This topic will include different algorithms for unsupervised data preprocessing such as data normalization, discretization, dimensionality reduction and feature extraction (PCA, ICA, SVD, linear and non linear, multidimensional scalling and non negative matrix factorization)
3. Aprenentatge no supervisat/Taxonomia numerica
   This topic will include classical and current algorithms for unsupervised learning from machine learning and statistics including hierarchical and parititional algorithms (K-means,Fuzzy C-means, Gaussian EM, graph partitioning, density based algorithms, grid based algorithms, unsupervised ANN, affinity propagation, ...)
4. Clustering Semisupervisat
   This topic will include current semi supervised algorithms for clustering data (based on constraints, based on rules, markov random fields)
5. Aprenentatge no supervisat en mineria de dades
   This topic will include current trends on knowledge discovery for data mining and big data, (scalability, any time clustering, one pass algorithms, approximation algorithms, distributed clustering, ..)
6. Regles d'associació
   This topic will include and introduction to association rules algorithms and their relationship with unsupervised learning algorithms and clustering
7. Mineria de dades sequencials i estucturades
   This topic will include algorithms for unsupervised learning with sequential data and structured data, such as mining frequent sequences, strings, time series clustering and frequent motifs, clustering data streams, clustering graphs and social networks and discovering frequent subgraphs
8. Fonaments de Raonament Basat en Casos
   Cognitive theories. Basic cycle of CBR reasoning. Academic Demosntrators.
9. CBR System Components
   Case Structure. Case Library Structure. Retrieval. Adaptation/Reuse. Evaluation/Repair. Learning/Retain
10. CBR Application
    A complex real-world example. OPENCASE/GESCONDA-CBR: a domain-independent CBR System .
11. CBR Development Problems
    Competence. Space Efficiency. Time Efficiency.
12. Reflective/Introspective Reasoning in CBR
    Introspection reasoning. Case Base maintenance.
13. CBR Applications and CBR Software Tools
    Industrial applications. Software tools. Recommender systems.
14. CBR System Evaluation
    Technical criteria. Ergonomic criteria.
15. Advanced Research Issues in CBR
    Temporal CBR. Spatial CBR. Hybrid CBR systems.

Metodologia docent

Mètode d'avaluació