Modern technologies in experimental sciences and computational sciences generate a large amount of high-dimensional data. In contrast to classical hypothesis testing, in modern statistical methods data are used to construct a statistical model that generated the dataset. Such a model not only describes the statistical properties of the past observations but also enables the prediction of future observations. The course is designed for students who wish to learn the mathematical methods to analyze high-dimensional data for active inference.
This course is based on the second half of the previous course "B07 Statistical Methods".
By completing this course, students will receive one credit.
This course can be taken immediately after the course B31 Statistical Testing, or as a stand-alone course.
Basic knowledge and skills in linear algebra (matrix, eigenvalues, eigenvectors, etc.), calculus (differentiation and integration of functions), and probability theories (Gaussian and some other distributions) are required. Skills in Python programming are also required for exercises.