What Every Engineer Should Know About Data-Driven Analytics

2 Mathematical Background for Predictive Analytics

DOI: 10.1201/9781003278177-2

In this chapter, we present the mathematical foundations required by data scientists to perform predictive analytics. The topics include basics concepts of linear algebra such as introduction to vectors; matrices, determinants, and equations for simple linear regression (SLR); dimensionality reduction techniques including Principal Component Analysis (PCA) and Singular Value Decomposition (SVD); and mathematical foundations for neural networks that will lay the foundations for the deep learning architectures discussed in the latter chapters.

Basics of Linear Algebra

Linear algebra is a field of mathematics that is a prerequisite for understanding machine learning ...