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.
Introduction to the Week
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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
As we introduced the operation of differentiation, it is essential to think about the inverse procedure - the integration. We start the module with basic definition of the integration and, as usual, all techniques required to calculate wide range of the indefinite integrals, stressing out that the result is not guaranteed now. Then we proceed with the idea and formal definition of area under curve and its relation to the indefinite case - the fundamental theorem of calculus. We finish our week with the discussion of the areas of infinite figures (improper integrals) and numerical methods to assess the value of the definite integral.
Taught By
Anton Savostianov
Lecturer
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