GitHub - leandromineti/ml-feynman-experience: A collection of statistical models...
source link: https://github.com/leandromineti/ml-feynman-experience
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README.md
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
- Law of large numbers
- Markov chains
- Single parameter frequentist inference
- Simple linear regression
- Multiple linear regression
Work in progress
To do
- Principal component analysis
- Linear discriminant analysis
- Central limit theorem
- Single parameter bayesian inference
- Decision tree
- Random Forest
- Support vector machine
- Perceptron
- Gradient boosting machine
- 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.
- 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.
- Cover image: Dr. Richard Feynman during the Special Lecture: the Motion of Planets Around the Sun. Public Domain. Created: 13 March 1964.
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