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.
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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
Since we now know limits, let us use them in order to define some instantaneous characteristics of functions starting with its slope. Thus we define function's derivative and discuss all the machinery to calculate it. Since it is a purely technical issue, you are expected to be able to do it: in order to make sure that you can find a derivative we provide a drill. This skills could be used for finding approximate values via linear approximation or during the search for extremal values. To provide an understanding of the sufficient condition of the extremum, we introduce the concept of convexity.
Taught By
Anton Savostianov
Lecturer
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