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Bibliography

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Axler, S. (2024). Linear algebra done right (4th ed.). Springer. https://linear.axler.net/

Cook, S. A. (1971). The complexity of theorem-proving procedures. Proceedings of the Third Annual ACM Symposium on Theory of Computing, 151–158. https://doi.org/10.1145/800157.805047

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Hartman, G. (2011). Fundamentals of matrix algebra (3rd ed.). http://www.apexcalculus.com/other-texts

Hastie, T., Tibshirani, R., & Friedman, J. (2017). The elements of statistical learning [ESL]: Data mining, inference, and prediction (2nd ed.). Springer. https://hastie.su.domains/ElemStatLearn/

James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An introduction to statistical learning [ISLR] with applications in Python. Springer. https://www.statlearning.com/

Karp, R. M. (1972). Reducibility among combinatorial problems. Symposium on the Complexity of Computer Computations. https://doi.org/10.1007/978-1-4684-2001-2_9

Levin, O. (2021). Discrete mathematics: An open introduction (3th Edition, 5th Printing). Self-published. http://discrete.openmathbooks.org

Poole, D. L., & Mackworth, A. K. (2017). Artificial intelligence: Foundations of computational agents (2nd ed.). University of British Columbia; Cambridge University Press. https://artint.info/

Weisstein, E. W. (n.d.). Vector addition. MathWorld: A Wolfram web resource. https://mathworld.wolfram.com/VectorAddition.html



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