BRUTTI PIERPAOLO
(programma)
1. Review of basic probability and inference.
2. Concentration of measure.
3. Basics of convex optimization.
4. Statistical functional: bootstrap & subsampling.
5. Nonparametric Regression and Density estimation: kernels and RKHS.
6. Nonparametric Classification.
7. Nonparametric Clustering: k-means, density clustering.
8. Graphical Models and their applications: parametric and nonparametric approaches.
9. Hints of Nonparametric Bayes
10. Minimaxity & Sparsity Theory.
 Riferimenti principali
- Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (2013). An Introduction to Statistical Learning with Applications in R.
Available at: http://www-bcf.usc.edu/~gareth/ISL/
- Larry Wasserman (2005). All of Nonparametric Statistics.
- Stephen Boyd and Lieven Vandenberghe. Convex Optimization.
Available at: http://stanford.edu/~boyd/cvxbook/
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