Bibliografía

La bibliografía recomendada está en su mayoría disponible y podrá consultarse en la biblioteca del laboratorio SINC(i) de la Faculta de Ingeniería y Ciencias Hídricas. Se recomienda, además, revisar la bibliografía citada por los textos sugeridos y en las transparencias de las clases.

Bibliografía específica

  • Ethem Alpaydin, Introduction to Machine Learning. The MIT Press: Adaptive Computation and Machine Learning series, 2010.
  • S. Marsland, Machine Learning: An Algorithmic Perspective. Chapman & Hall/CRC: Machine Learning & Pattern Recognition Series, 2009.
  • R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, MIT Press, Cambridge MA, 1998.
  • Christopher M. Bishop, Pattern Recognition and Machine Learning. Springer: Information Science and Statistics, 2006.
  • V. Cherkassky, F. Mulier, Learning from Data: Conceps, Theory and Methods. Wiley-International Science, 1998.
  • A. Cichocki and S. Amari, Adaptive Blind Signal and Image Processing. John Wiley & Sons, 2002.
  • R. O. Duda, P. E. Hart, D. G. Stork, Pattern Classification. Wiley-Interscience, 2001.
  • X. D. Huang, Y. Ariki, M. A. Jack, Hidden Markov models for speech recognition. Edinburgh University Press, 1990.
  • Hyvärinen, J. Karhunen, E. Oja, Independent Component Analysis. John Wiley & Sons, 2001.
  • Hyvärinen, J. Karhunen, E. Oja, Independent Component Analysis. John Wiley & Sons, 2001.
  • D. J. C. MacKay, Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2003.
  • B. D. Ripley, Pattern Recognition and Neural Networks, Cambridge University Press, 1999.
  • J. R. Quinlan, C4.5: Programs for Machine Learning. 1993.
  • R. P. N. Rao, B. A. Olshausen, M. S. Lewicki (Eds.), Probabilistic Models of the Brain: Perception and Neural Function. MIT Press, 2002.
  • V. N. Vapnik, The Nature of Statistical Learning Theory,.Springer, 2000.
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