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1. Introduction
2. Introduction to Probabilistic Methods and Monte Carlo Simulations
3. Linear Models
4. Data Preprocessing, Local Models and Tree-Based Models
5. Neural Networks for Regression
6. Ensembles of models and feature selection
7. Classification
8. Conclusion
9. Bibliography
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