Hastie, T.; Tibshirani, R.; Friedman, J.The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2ª ed. Springer, 2009. Gratuito em hastie.su.domains/ElemStatLearn.
James, G.; Witten, D.; Hastie, T.; Tibshirani, R.An Introduction to Statistical Learning with Applications in Python. Springer, 2023. Gratuito em statlearning.com.
Bishop, C. M.Pattern Recognition and Machine Learning. Springer, 2006.
Murphy, K. P.Probabilistic Machine Learning: An Introduction. MIT Press, 2022. Gratuito em probml.github.io/pml-book.
Géron, A.Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. 3ª ed. O'Reilly, 2022.
Mitchell, T. M.Machine Learning. McGraw-Hill, 1997 — a definição clássica de um programa que aprende.
Artigos & fontes históricas
Regressão
Legendre, A.-M.Nouvelles méthodes pour la détermination des orbites des comètes. Courcier, 1805 — primeira publicação dos mínimos quadrados, no apêndice Sur la Méthode des moindres quarrés. Texto completo (archive.org)
Gauss, C. F.Theoria motus corporum coelestium in sectionibus conicis solem ambientium. Perthes & Besser, 1809 — onde Gauss afirma usar os mínimos quadrados desde 1795, abrindo a disputa de prioridade com Legendre. Texto completo (archive.org)
Galton, F. "Regression towards Mediocrity in Hereditary Stature." Journal of the Anthropological Institute 15 (1886): 246–263 — o artigo que deu nome à regressão. PDF (galton.org) · DOI
Hanley, J. A. "'Transmuting' Women into Men: Galton's Family Data on Human Stature." The American Statistician 58, n. 3 (2004): 237–243 — a reanálise moderna que recupera os momentos de Galton e explica a construção da média dos pais. DOI
Anscombe, F. J. "Graphs in Statistical Analysis." The American Statistician 27, n. 1 (1973): 17–21 — o quarteto. DOI
Cook, R. D. "Detection of Influential Observation in Linear Regression." Technometrics 19, n. 1 (1977): 15–18 — a distância de Cook. DOI
Hoerl, A. E.; Kennard, R. W. "Ridge Regression: Biased Estimation for Nonorthogonal Problems." Technometrics 12, n. 1 (1970): 55–67. DOI
Outras fontes históricas
Turing, A. M. "Computing Machinery and Intelligence." Mind (1950).
Rosenblatt, F. "The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain." Psychological Review (1958).
Cortes, C.; Vapnik, V. "Support-Vector Networks." Machine Learning (1995).
Breiman, L. "Random Forests." Machine Learning (2001).
Friedman, J. H. "Greedy Function Approximation: A Gradient Boosting Machine." Annals of Statistics (2001).
Chen, T.; Guestrin, C. "XGBoost: A Scalable Tree Boosting System." KDD (2016).
Lundberg, S.; Lee, S.-I. "A Unified Approach to Interpreting Model Predictions." NeurIPS (2017) — SHAP.
Grootendorst, M. "BERTopic: Neural topic modeling with a class-based TF-IDF procedure." arXiv:2203.05794 (2022).
Software & documentação
scikit-learn — a biblioteca de referência para ML clássico em Python.