Multivariate mathematics for data science

What it covers

This unit introduces and develops a range of basic concepts and techniques related to two main subjects: multivariate calculus and linear algebra. The programme is targeted for students following a degree in data science and the material will have emphasis on assimilating important principles, on developing classical techniques, and on facilitating the use of the theoretical framework and practical methods in the context of common applicative problems. The unit will cover partial derivatives, extrema of multivariate functions, integration, linear transformations, matrices and orthogonalisation, eigenvalues and eigenvectors, and applications to data science.

Assessment

When and where