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.