In this notebook we will examine the method of Principal Component Analysis (PCA), which can be used to project data onto lower-dimensional spaces while maximizing the variance of the projections. PCA works by projecting the data onto the subspace spanned by the eigenvectors of the covariance matrix associated with the largest eigenvalues. We b... Read more 14 Jun 2018 - 3 minute read

This is the second notebook I write related to linear regression, because it’s time to apply this model to a real dataset, starting with the Boston housing dataset. In this problem we want to predict the median value of houses given 13 input variables. %matplotlib inline from sklearn.datasets import load_boston import numpy as np np.random.seed... Read more 12 Jun 2018 - 2 minute read

In this notebook we will examine different ways to train linear models for a regression problem. In this problem we have a dataset of N input variables $\mathbf{X} = \lbrace\mathbf{x}_1, …, \mathbf{x}_N\rbrace$ together with their target values $\mathbf{T} = \lbrace\mathbf{t}_1, …, \mathbf{t}_N\rbrace$, and we would like to learn a model that ca... Read more 11 Jun 2018 - 5 minute read