Advanced course, prerequisites: Bachelor degree with at least 60 ECTS in statistics, and STAN41 or a course in linear algebra with matrix calculus.
In many practical problems originating from areas like geophysical sciences,
technology, such measurements are becoming more and more frequent, thus pushing the boundaries of high-dimensional data analysis. In data science, high dimensional problems are often approached through dimension free analysis by treating an observation as a function. This is one way to circumvent the technical aspects identified with analysing high-dimensional data, and thus avoiding the curse of dimensionality. This course is meant to introduce the students to various aspects of dimension free analysis, which obviates the technical and computational hurdles associated with high dimensional data. This way of dealing with high dimensional data is an emerging and rapidly developing field that requires understanding both established methods and newly adopted techniques. The primary objective of this course will be to focus on the application of functional data analysis techniques to real world problems, and thus, mathematical rigour is often traded for adaptability to