The goal of this course is to give an introduction to machine learning. We will introduce several successful methods for regression, classification, clustering, and data dimension reduction and develop the mathematical theory behind these methods, with a focus on continuous optimization theory. In the exercise sessions, the students will work on proof-style exercises, as well as implementation exercises in Python.
The course builds on knowledge acquired in the first year, in particular Calculus and Linear Algebra 1 and 2, Analysis, Probability (or its predecessor Introduction to Probability and Statistics) and Computer Programming for Mathematics.
This course is one of the modelling courses in the bachelor programme with major ‘mathematics’ or ‘mathematics and applications’. Please find more information on the students website.