Técnicas Avanzadas de Aprendizaje Automático

• Horas semanales
• Objetivos
• Contenidos
• Metodología docente
• Método de evaluación

Horas semanales

Objetivos

1. Conocer y utilizar tecnicas avanzadas de aprendizaje no supervisado y mineria de datos no supervisada para aplicaciones en todos los dominios de la ingenieria y la ciencia
   Competencias relacionadas: CB7, CT2, CT4, CEP1, CG3, CEA12, CEA13, CG1,

Contenidos

1. Data Mining, a global perspective
   Brief introduction to what is Data Mining and Knowledge Discovery, the areas they are related to and the different techniques involved
2. Unsupervised data preprocessing/transformation
   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. Unsupervised Machine Learning/Numerical Taxonomy
   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. Semi supervised clustering
   This topic will include current semi supervised algorithms for clustering data (based on constraints, based on rules, markov random fields)
5. Unsupervised methodologies in Knowledge Discovery and Data Mining
   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. Association Rules
   This topic will include and introduction to association rules algorithms and their relationship with unsupervised learning algorithms and clustering
7. Mining sequential and structured data
   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. Fundamentals of Case-Based Reasoning
   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.

Metodología docente

Método de evaluación