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Classical Optimization

As far as we can say about artificial intelligence, machine learning, and deep learning, there is a certain class of machine learning algorithms that seems to be more appropriate to be called optimization algorithms, namely: Decision Trees, Random Forest, k-Nearest Neighbors, and Support Vector Machines.
This Python notebook is a self-implementation (without any framework) in Python (maybe Julia in the future).

NOTE: The SVM implementation need more documentation TODO: Based on the SVM implementation do a quantum version.

References and resources

Kneusel, R. T. (2023). How AI Works: From Sorcery to Science. No Starch Press.
Lallensack, Jens N.; Romilio, Anthony; Falkingham, Peter L. (2022). Supplementary material from "A machine learning approach for the discrimination of theropod and ornithischian dinosaur tracks". The Royal Society Collection. https://doi.org/10.6084/m9.figshare.c.6272858.v1

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Implementing a diverse range of classical machine learning and optimization without frameworks.

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