Foundations of Machine Learning

The problem of statistical learning. Training versus test (Vapnik-Chervonenkis dimension, training and generalization). Linear model (linear, non-linear and logistic regression). What it is and how to detect and deal with overfitting. Machine learning principles: Ocam razor, sample bias and data snooping. Similarity-based methods (nearest neighbor, radial basis functions, density estimation). Neural networks (MLP, training, approximation and regularization). Support vector machines. Aggregation methods. Selection of variables.

Basic Information

60 hours
Mathematical Statistics


  • Abu-Moustafa, Y.S., Magdon-Ismail, M. e Lin H-S. (2012). Learning from data.
  • Murphy, K. P. (2013). Machine learning: a probabilistic perspective. The MIT Press.
  • Hastie, T., Tibishirani, R., & Friedman, J. (2002). The elements of statistical learning. Springer Series in Statistics.
  • Mohri, M.; Rostamizadeh, A. Foudantions of machine learning. MIT Press.
  • Devroye, Luc; Gyorfi, Laszló and Lugosi. Springer. Probabilistic theory of pattern recognition.


  • Efron, Bradley, and Trevor Hastie. Computer age statistical inference. Vol. 5. Cambridge University Press, 2016.
  • Kecman, Learning and Soft Computing MIT Press, 2001.
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