Foundations of Machine Learning

Informações Básicas

Carga horária: 

45h

Pré-requisito: 

Mathematical Statistics

Ementa: 

Probability tools, concentration inequalities; PAC model; Rademacher complexity, growth function, VC-dimension; Perceptron, Winnow Support vector machines (SVMs); Kernel methods; Boosting; On-line learning; Decision trees; Density estimation, maximum entropy models; Logistic regression, conditional maximum entropy models; Regression problems and algorithms; Ranking problems and algorithms; Learning languages and automata; Reinforcement learning, Markov decision processes (MDPs)

Bibliografia

Obrigatória: 

·       Mohri, M., Rostamizadeh, A., Talwalkar, A. (2012) Foundations of machine learning. MIT Press, Cambridge, MA.
·       Abu-Moustafa, Y.S., Magdon-Ismail, M., e Lin H-S. (2012) Learning from data. AMLBook.com.
·       Hastie, T., Tibshirani, R., Friedman, J. (2013) The elements of statistical learning. Springer.

Complementar: 

·       Devroye, L., Gÿorfi, L., e Lugosi, G. (1996) A probabilistic theory of pattern recognition. Springer-Verlag.
·       Murphy, K.P. (2013) Machine learning: a probabilistic perspective. MIT Press, Cambridge, MA.
·       Hastie, T., Tibshirani, R., Wainwright, M. (2015) Statistical learning with sparsity. CRC Press.
·       Efron, Bradley, and Trevor Hastie. Computer age statistical inference. Vol. 5. Cambridge University Press, 2016.
·       Kecman, Learning and Soft Computing MIT Press, 2001.