Bibliography

Bibliography

Bur19

Andriy Burkov. The hundred-page machine learning book. Volume 1. Andriy Burkov Quebec City, QC, Canada, 2019.

CAB+22

The Turing Way Community, Becky Arnold, Louise Bowler, Sarah Gibson, Patricia Herterich, Rosie Higman, Anna Krystalli, Alexander Morley, Martin O'Reilly, Kirstie Whitaker, and others. The Turing way: a handbook for reproducible data science. Zenodo, 2022.

HoR99

U.S. House-of-Representatives. Systems development life-cycle policy. U.S. House of Representatives, 1999. https://web.archive.org/web/20131019091833/http://www.house.gov/content/cao/procurement/ref-docs/SDLCPOL.pdf.

JWHT21

Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An Introduction to Statistical Learning. Springer, 2nd edition, 2021. https://www.statlearning.com/.

LL17

Scott M Lundberg and Su-In Lee. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems (NeurIPS), 4768–4777. 2017.

OD21

Florian Ostmann and Cosmina Dorobantu. AI in financial services. Alan Turing Institute. doi, 2021. https://www.turing.ac.uk/sites/default/files/2021-06/ati_ai_in_financial_services_lores.pdf.

RSG16

Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. "Why should i trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (KDD), 1135–1144. 2016.