1.
Casella, G., Berger, R.L.: Statistical inference. Cengage Learning, Delhi (2017).
2.
Abu-Mostafa, Y.S., Magdon-Ismail, M., Lin, H.-T.: Learning from data: a short course. AMLBook.com, [United States] (2012).
3.
Grimmett, G., Stirzaker, D.: Probability and random processes. Oxford University Press, Oxford (2001).
4.
Mood, A.M., Graybill, F.A., Boes, D.C.: Introduction to the theory of statistics. McGraw-Hill Book Company, [Auckland?] (1974).
5.
Hastie, T., Tibshirani, R., Friedman, J.H.: The elements of statistical learning: data mining, inference, and prediction. Springer, New York (2009).
6.
Murphy, K.P.: Machine learning: a probabilistic perspective. MIT Press, Cambridge, Mass (2012).
7.
Barber, D.: Bayesian reasoning and machine learning. Cambridge University Press, Cambridge (2012).
8.
Bishop, C.M.: Pattern recognition and machine learning. Springer, Oxford (2006).
9.
Hastie, T., Tibshirani, R., Friedman, J.H.: The elements of statistical learning: data mining, inference, and prediction : with 200 full-color illustrations. Springer, New York (2001).
10.
Goodfellow, I., Bengio, Y., Courville, A.: Deep learning. The MIT Press, Cambridge, Massachusetts (2016).
11.
Raschka, S.: Python machine learning: unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics. Packt Publishing, Birmingham (2015).
12.
McKinney, W.: Python for data analysis. O’Reilly, Farnham (2013).
13.
Kevin Sheppard - Lecture Notes, https://www.kevinsheppard.com/Main_Page.
14.
Harrington, P.: Machine learning in action. Manning Publications, Shelter Island, N.Y. (2012).
15.
Source Code for the book: Machine Learning in Action published by Manning, https://github.com/pbharrin/machinelearninginaction.
16.
Zumel, N., Mount, J.: Practical data science with R. Manning, Shelter Island, New York (2014).
17.
Nolan, D.A., Lang, D.T. eds: Data science in R: a case studies approach to computational reasoning and problem solving. Chapman & Hall/CRC, Boca Raton, FL (2015).
18.
Kabacoff, R.: R in action: data analysis and graphics with R. Manning, Shelter Island, NY (2015).
19.
Wickham, H.: ggplot2: elegant graphics for data analysis. Springer, New York (2009).
20.
Wickham, H.: Advanced R. Chapman & Hall/CRC, Boca Raton, FL (2014).
21.
Sarkar, D.: Lattice: multivariate data visualization with R. Springer, New York (2008).
22.
Lantz, B.: Machine learning with R: learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications. Packt Publishing Limited, Birmingham (2013).
23.
Wickham, H.: R packages. O’Reilly Media, Sebastopol, California (2015).
24.
Wickham, H.: Advanced R. Chapman & Hall/CRC, Boca Raton, FL (2014).
25.
Peng, R.: R Programming for Data Science. Lulu.com, Morrisville (2016).
26.
Martinez, W.L., Martinez, A.R.: Computational statistics handbook with MATLAB. Chapman & Hall/CRC, Boca Raton (2016).
27.
Kiusalaas, J.: Numerical methods in engineering with MATLAB. Cambridge University Press, Cambridge (2016).
28.
Karau, H., Konwinski, A., Wendell, P., Zaharia, M.: Learning Spark: lightning-fast big data analytics. O’Reilly, Sebastopol, CA (2013).
