  Preference modelling lies at the intersection of economics, decision theory,
machine learning and statistics. By understanding individuals' preferences and
how they make choices, we can build products that closely match their
expectations, paving the way for more efficient and personalised applications
across a wide range of domains. The objective of this tutorial is to present a
cohesive and comprehensive framework for preference learning with Gaussian
Processes (GPs), demonstrating how to seamlessly incorporate rationality
principles (from economics and decision theory) into the learning process. By
suitably tailoring the likelihood function, this framework enables the
construction of preference learning models that encompass random utility
models, limits of discernment, and scenarios with multiple conflicting
utilities for both object- and label-preference. This tutorial builds upon
established research while simultaneously introducing some novel GP-based
models to address specific gaps in the existing literature.
