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Asignaturas

Introducción al Aprendizaje Automático

By 31 mayo 2016 2 Comments

Introducción al Aprendizaje Automático (2 ECTS)

  1. Técnicas de aprendizaje supervisado y no supervisado
  2. Función de pérdida (Loss function), estrategias de validación (leaving-one-out, cross-validation).
  3. k-NN, Parzen Windows, KD-Trees, …
  4. Clustering paramétrico y no parámetrico: K-means, GMM …
  5. Modelos conexionistas: redes neuronales
  6. Deep Learning: RBM, Auto-encoders, DBN/DNN
  7. Learning from Data Streams

2 Comments

  • Saúl dice:

    Buenas tardes,

    ¿Sería posible que me enviaran la bilbiografía de esta asignatura?

    Muchas gracias y un saludo,
    Saúl

    • Jon Ander dice:

      Hola,

      Acabo de leer este comentario ahora. No los suelo revisar porque no esperamos comentarios por esta vía.
      Habitualmente no facilitamos esta información, pero como la asignatura que solicitas es la mía te lo voy a pasar en formato BibTeX de LaTeX.

      Saludos,

      Jon

      @book{Bishop2006,
      author = {Christopher M. Bishop},
      title = {Pattern Recognition and Machine Learning},
      year = {2006},
      publisher = {Springer},
      series = {Information Science and Statistics},
      }

      @book{Bishop1995,
      author = {Christopher M. Bishop},
      title = {Neural Networks for Pattern Recognition},
      year = {1995},
      publisher = {Oxford University Press},
      }

      @book{Duda2000,
      author = {Richard O. Duda and Peter E. Hart and David G. Stork},
      title = {Pattern Classification},
      year = {2000},
      publisher = {Addison-Wesley},
      edition = {Second},
      }

      @article{Pedregosa:11,
      author = { F. Pedregosa and G. Varoquaux and A. Gramfort and V. Michel and
      B. Thirion and O. Grisel and M. Blondel and P. Prettenhofer and
      R. Weiss and V. Dubourg and J. Vanderplas and A. Passos and
      D. Cournapeau and M. Brucher and M. Perrot and E. Duchesnay },
      title = {Scikit-learn: Machine Learning in {P}ython},
      journal = {{Journal of Machine Learning Research}, JMLR},
      volume = {12},
      pages = {2825–2830},
      year = {2011},
      }

      @article{geurts:06,
      author = {P. Geurts and D. Ernst and L. Wehenkel},
      title = {Extremely Randomized Trees},
      year = {2006},
      journal = {{Machine Learning}},
      ISSN = {0885-6125},
      volume = {63},
      number = {1},
      pages = {2–42},
      publisher = {Kluwer Academic Publishers},
      doi = {DOI 10.1007/s10994-006-6226-1},
      }

      @misc{wiki:ml,
      author = {Wikipedia},
      title = {{Machine Learning}},
      howpublished = {\url{http://en.wikipedia.org/wiki/Machine_learning}},
      year = {2015},
      note = {[Online: accessed April 2015]},
      }

      @misc{web:deeplearning,
      author = {Several authors},
      title = {{Deep Learning}},
      howpublished = {\url{http://www.deeplearning.net}},
      year = {2015},
      note = {[Online: accessed June 2015]},
      }

      @unpublished{Bengio-et-al-2015-Book,
      title={Deep Learning},
      author={Yoshua Bengio and Ian J. Goodfellow and Aaron Courville},
      note={Book in preparation for MIT Press},
      url={http://www.iro.umontreal.ca/~bengioy/dlbook},
      year={2015}
      }

      @misc{hinton:rbm-training-guide,
      title={A Practical Guide to Training Restricted Boltzmann Machines},
      author={Geoffrey Hinton},
      year={2010}
      }

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