Data Science and Machine Learning Mathematical and Statistical Methods - DataScienceCentral.com

Next, we cover Unsupervised Learning techniques such as density estimation, clustering, and principal component analysis. Important tools in unsupervised learning include the cross-entropy training loss, mixture models, the Expectation–Maximization algorithm, and the Singular Value Decomposition. This is followed by Regression. The purpose of this chapter is to explain the mathematical ideas behind regression models and their practical aspects.