Mathematics for Machine Learning and Data Science 🗝️

Este repositório possui anotações, resumos, fichamentos e insights pessoais sobre estudos. Ele não possui materiais derivados.

Mathematics Books 📚

Statistics

• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. Springer.
• Bussab, W. O., & Morettin, P. A. (2017). Estatística básica. Saraiva Uni.
• Bruce, P., Bruce, A., & Gedeck, P. (2020). Practical statistics for data scientists. O'Reilly Media.
• Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning. Springer.
• Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. Springer.

Linear Algebra

• Boyd, S., & Vandenberghe, L. (2018). Introduction to applied linear algebra: Vectors, matrices, and least squares. Cambridge University Press.
• Aggarwal, C. C. (2021). Linear algebra and optimization for machine learning. Springer Nature BV. Fichamento 📜
• Cohen, M. X. (2021). Linear algebra: Theory, intuition, code. Sincxpress BV.

Machine Learning

• Izbicki, R., & Santos, T. M. (2022). Aprendizado de máquina: Uma abordagem estatística. UICLAP.
• Deisenroth, M. P., Faisal, A. A., & Ong, C. S. (2020). Mathematics for machine learning. Cambridge University Press.
• Krause, A., & Hübotter, J. (2025). Probabilistic artificial intelligence. Retrieved from https://arxiv.org/abs/2502.05244
• Géron, A. (2019). Mãos à obra: Aprendizado de máquina com Scikit-Learn & TensorFlow. Alta Books.
• Murphy, K. P. (2023). Probabilistic machine learning: Advanced topics. MIT Press.
• Murphy, K. P. (2022). Probabilistic machine learning: An introduction. MIT Press.
• Peyré, G. (2021). Mathematical foundations of data sciences. CNRS & DMA.

Optimization

• Carter, M. W., Price, C. C., & Rabadi, G. (2029). Operations research: A practical introduction (2nd ed.). CRC Press.
• Bierlaire, M. (2018). Optimization: Principles and algorithms. EPFL Press.
• Poler, R., Mula, J., & Díaz-Madroñero, M. (2014). Operations research problems: Statements and solutions. Springer-Verlag.
• Martins, J. R. R. A., & Ning, A. (2021). Engineering design optimization. Cambridge University Pres
• Manual de uso da biblioteca Pyomo para Programação Matemática, Claudemir Woche V. Carvalho e Anselmo R. Pitombeira Neto

Bioinspired Optimization

• Eiben, A. E., & Smith, J. E. (2015). Introduction to evolutionary computing (2nd ed.). Springer.
• Eberhart, R. C., Shi, Y., & Kennedy, J. (2001). Swarm intelligence. Morgan Kaufmann.

Decision Theory

• Hansson, S. O. (2005). Decision Theory. A Brief Introduction. Royal Institute of Technology.
• Peterson, M. (2017). An Introduction to Decision Theory. (2nd ed.). Cambridge University Press.
• Takemura, K. (2014). Behavioral Decision Theory. Psycological and Mathematical Description of Human Choice Behavior. Springer.
• Bacci, S., & Chiandotto B. (2020). Introduction to Statistical Decision Theory. Utility Theory and Causal Analysis. CRC Press.

Causal Inference

Courses 🧑‍💻

• Linear Algebra for Machine Learning and Data Science
• Calculus for Machine Learning and Data Science
• Probability & Statistics for Machine Learning & Data Science

Resources 🧰

• Matemática Ensino Fundamental (BNCC) @ Khan Academy
• Matemática Ensino Médio (BNCC) @ Khan Academy
• Essence of linear algebra @ 3Blue1Brown
• Essence of calculus @ 3Blue1Brown
• Análise Real @ Mas você só estuda?

Licence

• Attribution-NonCommercial-ShareAlike 4.0 International CC BY-NC-SA