Machine Learning Feynman Experience
"What I cannot create, I do not understand" - Feynman.
This is a collection of concepts I tried to implement using only Python, NumPy and SciPy on Google Colaboratory. If you want to play with the code, feel free to copy the notebook and have fun.
Notebooks
Work in progress
To do
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[ ] Principal component analysis
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[ ] Linear discriminant analysis
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[ ] Central limit theorem
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[ ] Single parameter bayesian inference
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[ ] Decision tree
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[ ] Random Forest
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[ ] Support vector machine
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[ ] Perceptron
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[ ] Gradient boosting machine
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[ ] Autoregressive models
Contributions
If you spot a mistake or omission, please feel free to create a new issue.
References
- Casella, G., & Berger, R. L. (2002). Statistical inference (Vol. 2). Pacific Grove, CA: Duxbury.
- Costa, M. A. (2019). Tópicos em ciência dos dados: Introdução aos modelos paramétricos e seus aplicações utilizando o R. Bonecker.
- DeGroot, M. H., & Schervish, M. J. (2012). Probability and statistics. Pearson Education.
- Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (2nd ed). New York, NY: Springer.
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Cover image: Dr. Richard Feynman during the Special Lecture: the Motion of Planets Around the Sun. Public Domain. Created: 13 March 1964.