(Joint with Emmanuel Letouzé)

Program

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Presentations and Readings

1. Aprendizaje estadistico
   Aplicación: Predicción y Causalidad
   Lecturas:
   • Statistical Modeling: The Two Cultures Leo Breiman. Statistical Science, Vol. 16, No. 3. (Aug., 2001), pp. 199-215.
   • Prediction Policy Problems. Jon Kleinberg. Jens Ludwig. Sendhil Mullainathan. Ziad Obermeyer. American Economic Review. Vol. 105, NO. 5, May 2015. (pp. 491-95).
   • Risk Adjustment Revisited using Machine Learning Techniques Proceeding Series of the Brazilian Society of Computational and Applied. Download
     
2. Principales Técnicas Lineales, Principales Técnicas No Lineales, Selección y Validación de Modelos
   Aplicación: Discriminación Algorítmica
   Lecturas:
   • Three learning principles from Learning From Data (2012) , Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin .
   • Predictably Unequal? The Effects of Machine Learning on Credit Markets
     
     
3. Sesgo Algorítmico
   Aplicacion: Sesgos y Retroalimentacion Circular
   Lecturas:
   Discrimination in the Age of Algorithms
   Efficient allocation of law enforcement resources using predictive police patrolling
   To predict and serve?
   
   
4. Causalidad Fundamentos I, Causalidad Fundamentos II
   Lecturas:
   An Introduction to Causal Inference
   Economics in the Age of Big Data
   
   
5. Privacidad
   Lecturas:
   Calibrating Noise to Sensitivity in Private Data Analysis
   Collecting and Analyzing Multidimensional Data with Local Differential Privacy

References

Theory

[LS]: Luxburg, U., B. Scholkopf. 2008. Statistical Learning Theory: Models, Concepts and Results.
http://arxiv.org/abs/0810.4752

[HTF]: Hastie, T., Tibshirani, R. y J. Hastie. 2009. The Elements of Statistical Learning: Data Minning, Inference and Prediction. Segunda Edición. Springer
http://web.stanford.edu/~hastie/local.ftp/Springer/OLD/ESLII_print4.pdf

Applications