APPLIED LINEAR ALGEBRA

Catalog description

In this course you will learn the fundamentals of vectors, matrices, vector spaces, and linear transformations. You'll explore eigenvalues, eigenvectors, matrix factorizations, principal component analysis (PCA), and the singular value decomposition (SVD), applying these concepts to real-world data science scenarios such as dimensionality reduction and optimization. This course covers a large set of mathematical techniques essential to understand and implement various algorithms in data analytics, particularly related to machine learning.