Abu-Mostafa, Yaser S., Malik Magdon-Ismail, and Hsuan-Tien Lin. 2012. Learning from Data: A Short Course. [United States]: AMLBook.com.
Anon. n.d.-a. ‘Kevin Sheppard - Lecture Notes’. Retrieved (https://www.kevinsheppard.com/Main_Page).
Anon. n.d.-b. ‘Source Code for the Book: Machine Learning in Action Published by Manning’. Retrieved (https://github.com/pbharrin/machinelearninginaction).
Barber, David. 2012. Bayesian Reasoning and Machine Learning. Cambridge: Cambridge University Press.
Bishop, C. M. 2006. Pattern Recognition and Machine Learning. Vol. Information science and statistics. Oxford: Springer.
Casella, George, and Roger L. Berger. 2017. Statistical Inference. Vol. The Duxbury advanced series in statistics and decision sciences. Second edition. Delhi: Cengage Learning.
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. Vol. Adaptive computation and machine learning. Cambridge, Massachusetts: The MIT Press.
Grimmett, Geoffrey, and David Stirzaker. 2001. Probability and Random Processes. Third edition. Oxford: Oxford University Press.
Harrington, Peter. 2012. Machine Learning in Action. Shelter Island, N.Y.: Manning Publications.
Hastie, Trevor, Robert Tibshirani, and J. H. Friedman. 2001. The Elements of Statistical Learning: Data Mining, Inference, and Prediction : With 200 Full-Color Illustrations. Vol. Springer series in statistics. New York: Springer.
Hastie, Trevor, Robert Tibshirani, and J. H. Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Vol. Springer series in statistics. 2nd ed. New York: Springer.
Kabacoff, Robert. 2015. R in Action: Data Analysis and Graphics with R. Second edition. Shelter Island, NY: Manning.
Karau, Holden, Andy Konwinski, Patrick Wendell, and Matei Zaharia. 2013. Learning Spark: Lightning-Fast Big Data Analytics. Sebastopol, CA: O’Reilly.
Kiusalaas, Jaan. 2016. Numerical Methods in Engineering with MATLAB. Third edition. Cambridge: Cambridge University Press.
Lantz, Brett. 2013. Machine Learning with R: Learn How to Use R to Apply Powerful Machine Learning Methods and Gain an Insight into Real-World Applications. Birmingham: Packt Publishing Limited.
Martinez, Wendy L., and Angel R. Martinez. 2016. Computational Statistics Handbook with MATLAB. Vol. Chapman&Hall/CRC computer science and data analysis series. Third edition. Boca Raton: Chapman & Hall/CRC.
McKinney, Wes. 2013. Python for Data Analysis. Farnham: O’Reilly.
Mood, Alexander McFarlane, Franklin A. Graybill, and Duane C. Boes. 1974. Introduction to the Theory of Statistics. Vol. McGraw-Hill series in probability and statistics. Third edition. [Auckland?]: McGraw-Hill Book Company.
Murphy, Kevin P. 2012. Machine Learning: A Probabilistic Perspective. Vol. Adaptive computation and machine learning. Cambridge, Mass: MIT Press.
Nolan, Deborah Ann, and Duncan Temple Lang, eds. 2015. Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving. Vol. Chapman&Hall/CRC the R series. Boca Raton, FL: Chapman & Hall/CRC.
Peng, Roger. 2016. R Programming for Data Science. Morrisville: Lulu.com.
Raschka, Sebastian. 2015. Python Machine Learning: Unlock Deeper Insights into Machine Learning with This Vital Guide to Cutting-Edge Predictive Analytics. Vol. Community experience distilled. Birmingham: Packt Publishing.
Sarkar, Deepayan. 2008. Lattice: Multivariate Data Visualization with R. Vol. Use R! New York: Springer.
Wickham, Hadley. 2009. Ggplot2: Elegant Graphics for Data Analysis. Vol. Use R! New York: Springer.
Wickham, Hadley. 2014a. Advanced R. Vol. Chapman&Hall/CRC the R series. Boca Raton, FL: Chapman & Hall/CRC.
Wickham, Hadley. 2014b. Advanced R. Vol. Chapman&Hall/CRC the R series. Boca Raton, FL: Chapman & Hall/CRC.
Wickham, Hadley. 2015. R Packages. Sebastopol, California: O’Reilly Media.
Zumel, Nina, and John Mount. 2014. Practical Data Science with R. Shelter Island, New York: Manning.