About this course

This online book consists of lecture notes of the course which will I taught during three weeks from 25 March to 12 April 2024 to the inaugural MSc “AI for Science” cohort at the African Institute for Mathematical Sciences (AIMS), South Africa.

Content

In this course we will cover such topics as Bayesian inference, hierarchical modelling, Gaussian processes for spatial statistics, ordinary differential equations for disease transmission modelling.

We will build probabilistic models and perform inference using a probabilistic programming language Numpyro in a fully Bayesian manner to characterise uncertainty of the modelled quantities.

Although the course is primarily computational in nature, the models which we will examine are inspired by the typical modelling practices found in epidemiology.

Prerequisites

• Conditional probability

• Joint probability

• Marginal distribution

• Python: numpy, matplotlib

Evaluation

Throughout the course, you will see a set of Tasks in the lecture notes. You need to submit answers to all of the tasks in one executable file (link to a Colab document or an .ipynb).

Tasks are to be solved individually and submitted. Group tasks are to be discussed with your peers during the class.

Task

This is an example of the Task section.