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

• Generalized linear models
• Expectation maximization

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.
• 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.
• Cover image: Dr. Richard Feynman during the Special Lecture: the Motion of Planets Around the Sun. Public Domain. Created: 13 March 1964.