Uncertainty: Probabilistic Models in the Natural Sciences

Module Leader:
Balázs Török
2016-2017 Spring
Immersion 1
Natural Sciences, Numerical Sciences

A doctor wants to identify what disease, what hidden cause is producing a patient’s symptoms. Different diseases, however, can produce similar symptoms. In science, we seek a description, a model of the world, but many different models could produce similar measurements. In poker, we want to know whether the other players are bluffing. Usually, the information we are provided is ambiguous. Regardless of whether what we see is inherently random or just not known to us, we would like to have a framework that enables us to reason about such uncertain phenomena. A thorough consideration of what properties such a framework should have will lead us to probability theory. In this module, we will set out the basics of probability theory and will try to formalise our beliefs. Then, we will look at how we can make inferences, in other words, rational guesses about the unknown. We will also discuss some inevitable difficulties in learning about the world. Finally, we will look at some examples of how this framework is applied in cognitive science and discuss some recent findings.

The module is suitable for students interested in natural sciences, especially cognitive science, psychology and even machine learning. Evaluation is based on the students’ independent work in the form of homework and a final essay.

The main objectives of the modules are a) to introduce students to the probabilistic framework, b) to understand how probability theory and statistics may be applied to science, and c) to familiarise them with some basic ideas in computational cognitive science and machine learning.