Notes and notebooks on ML

ML 01 - Linear Regression

In this notebook we express the cost function and the gradient of a linear regressor in a vectorized form. We analitically solve the normal equation for the best fit parameters, and show how the model can be applied to the non-linear fit of atmospheric CO₂ levels over time.

ML 02 - Gradient Descent

We cast the gradient of a linear regressor in a vectorized form, and test the result of a gradient descent optimization algorithm by comparing our result the analytical solution found in the previous notebook. We review the importance of feature normalization.

ML 03 - Logistic Regression 1 (warm-up)

This time we cannot derive an analytical solution to test our numerical algorithm. Therefore, we test our classifier on a simple data set with only two features that we can easily visualize.

ML 04 - Logistic Regression 2

We use the algorithm developed and tested in our previous notebook to reproduce cancer diagnosis classification results found in the medical literature.