Hi! Our course aims to provide necessary background in Calculus sufficient for up-following Data Science courses. Course starts with a basic introduction to concepts concerning functional mappings. Later students are assumed to study limits (in case of sequences, single- and multivariate functions), differentiability (once again starting from single variable up to multiple cases), integration, thus sequentially building up a base for the basic optimisation. To provide an understanding of the practical skills set being taught, the course introduces the final programming project considering the usage of optimisation routine in machine learning. Additional materials provided during the course include interactive plots in GeoGebra environment used during lectures, bonus reading materials with more general methods and more complicated basis for discussed themes.
Partial Derivatives: Definition
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It was great to deal with this course as it helped me in gaining a much and important details and knowledge behind ML. Thanks a lot!
A really good course for refreshing basic math and understanding the reason why Gradient Descent is used
Whilst we have discussed all linear related concepts for single variate functions, it is essential to try and generalise it for the multivariate case. Since the derivative concept is hard to stretch directly, we start with the idea of linear approximation and tangent plane; thus we introduce partial derivatives and the differentiability. We separately spend sometime discussing neural network inspired composite multivariate functions and all-mighty chain rule. Our generalisation attempt finalised with the idea of convexity in terms of the second partial derivatives.
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Anton Savostianov
Lecturer
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