This course starts with jumping straight into a real-world problem of ranking products/services/... From there we will dive into the whole idea of Systematically Updating beliefs using actually discrete probability tools. Then we will recall some continuous world definitions and get to a continuous version of the Bayes' Theorem. This is where things get significantly more challenging: the generalisation of the Bayes' theorem with its applications are not easy to digest. We will take things slowly, but don't expect it to be super easy. Moreover, we will even talk about the fundamental of making decisions under uncertainty which will practically lead to the very introduction of Machine Learning. The ideas and the whole set up presented in this course are ultra applicable.

Moreover, this course has two modes: "with Python" or "just maths". The former option has the same maths content as the second one, but adds an introduction to the popular basic Python tools on top of it.