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

Workload
45 hours
Requirements
Mathematical Statistics

Mandatory: 

  • Abu-Moustafa, Y.S., Magdon-Ismail, M. e Lin H-S. (2012). Learning from data. AML-Book.com.
  • 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.

Complementary: 

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