Imperial College London »Mathematics for Machine Learning« Specialization on Coursera (completed September, 2018)
A sequence of 3 courses on the prerequisite mathematics for applications in data science and machine learning. Successful participants learn how to represent data in a linear algebra context and manipulate these objects mathematically. They are able to summarise properties of data sets and map them onto lower dimensional spaces with principal component analysis. Finally they can solve optimisation problems and use this skill to train models for describing data such as simple neural networks.
❶ In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them. (David Dye, Professor of Metallurgy)
❷ The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting. (Samuel J. Cooper, Lecturer, Dyson School of Design Engineering)
❸ The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require basic Python and numpy knowledge. At the end of this specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning. (Dr.-Ing Marc Deisenroth, Lecturer in Statistical Machine Learning, Department of Computing)
(Recommended) Mary L Boas (2006) »Mathematical Methods in the Physical Sciences«, John Wiley and Sons, 3rd Ed
(Recommended) Marc Peter Deisenroth et al (2020) »Mathematics for Machine Learning«, Cambridge University Press, 1st Ed, ISBN-13: 978-1108455145
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