Contenido sugerido

1. Métodos de clasificación
2. Matrices dispersas
3. Métodos de búsqueda de estructura
4. Agrupamiento de datos
5. Técnicas de aprendizaje máquina para el análisis de datos

Sugerencias de Bibliografia

1. Bandeira, A. S.  (2016) Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science. http://www.cims.nyu.edu/bandeira/TenLecturesFortyTwoProblems.pdf
2. Bishop, C.M.  (2006) Pattern Recognition and Machine Learning. Springer.
3. Buhlmann, P., Drineas, P., Kane, M. and van der Laan, M.  (2016) Handbook of Big Data. CRC/Chapman and Hall.
4. Buhlmann, P. and van de Geer, S.  (2016) Statistics for High-Dimensional Data. Springer.
5. Fukunaga, K.  (1990) Introduction to Statistical Pattern Recognition. Academic Press.
6. Giraud, C.  (2015) Introduction to High-Dimensional Statistics. CRC/Chapman and Hall.
7. Hastie, T., Tibshirani, R. and Friedman, J.H.  (2008). The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer.
8. Hjsgaard, S., Edwards, D. and Lauritzen, S.  (2012). Graphical Models with R. Springer.
9. Izenman, A.J.  (2008) Modern Multivariate Statistical Techniques. Springer.
10. Mahoney, M.  (2016) Lecture Notes on Randomized Linear Algebra. https://arxiv.org/abs/1608.04481
11. National Research Council  (2013) Frontiers in Massive Data Analysis. The National Academies Press.
12. Nolan, D. and Temple Lang, D.  (2015) Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving. CRC/Chapman and Hall.
13. Pourahmadi, M.  (2013) High-Dimensional Covariance Estimation. Wiley.