David Dye

David Dye is a Professor of Metallurgy in the Department of Materials. He develops alloys for jet engines, nuclear and caloric materials so as to reduce fuel burn and avoid in-service failure. This involves crystallography (vectors and transformation matrices) and techniques like neutron and synchrotron X-ray diffraction and electron microscopy at the atomic scale. These give rise to 'big data' analysis problems associated simply with the amounts of data we can now collect. His Phd and undergraduate degrees were from Cambridge University in 1997 and 2000; he joined Imperial in 2003. He also teaches introductory mathematics - errors and data analysis, and has won student-led awards for innovation in teaching.

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Mathematics for Machine Learning: Multivariate Calculus (Coursera)

This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient of a function. [...]

Mathematics for Machine Learning: Linear Algebra (Coursera)

In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these [...]