Foundations of Machine Learning

Basic information

Workload: 

45 hours

Prerequisite: 

Mathematical Statistics

Syllabus: 

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.

Bibliography

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.