11. Bibliography

[CZ18]

B. Chambers and M. Zaharia. Spark: The Definitive Guide: Big Data Processing Made Simple. O'Reilly Media, 2018. ISBN 9781491912300. URL: https://books.google.com.br/books?id=oitLDwAAQBAJ.

[HTF09]

T. Hastie, R. Tibshirani, and J.H. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer series in statistics. Springer, 2009. ISBN 9780387848846. URL: https://books.google.com.br/books?id=eBSgoAEACAAJ.

[Mat15]

E. Matthes. Python Crash Course: A Hands-On, Project-Based Introduction to Programming. No Starch Press, 2015. ISBN 9781593276034.

[Mit97]

Tom M Mitchell. Machine learning. Volume 1. McGraw-hill New York, 1997.

[Ram15]

L. Ramalho. Fluent Python: Clear, Concise, and Effective Programming. O'Reilly Media, 2015. ISBN 9781491946251.

[RRN20]

S.J. Russell, S. Russell, and P. Norvig. Artificial Intelligence: A Modern Approach. Pearson series in artificial intelligence. Pearson, 2020. ISBN 9780134610993. URL: https://books.google.com.br/books?id=koFptAEACAAJ.

[SB18]

R.S. Sutton and A.G. Barto. Reinforcement Learning, second edition: An Introduction. Adaptive Computation and Machine Learning series. MIT Press, 2018. ISBN 9780262039246. URL: https://books.google.com.br/books?id=sWV0DwAAQBAJ.

[Whi12]

T. White. Hadoop: The Definitive Guide. O'Reilly Media, 2012. ISBN 9781449338770. URL: https://www.oreilly.com/library/view/hadoop-the-definitive/9780596521974/.