Learn the mathematical methods to analyze high-dimensional data for statistical inference. Use large data sets to construct a statistical model that not only describes the dataset but also allows for prediction of future data. Progress from simple regression models through more sophisticated techniques for dimensional reduction, categorization, and decision making. Use Bayesian approaches and clustering techniques as an introduction to machine learning. Weekly exercises and homework consolidate this learning.
This course provides an introduction to machine learning. Students are required to have some knowledge and skills in mathematics. However, the course is not intended for pure theorists.
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.