Purpose

The purpose of today is to get hands on experience in applying Bayesian, probabilistic models to HCI. The focus is on specific, practical examples rather than theory.

Outline

We will cover two topics, with a mix of practical and lecture material.

Direct links to the notebooks for each of the summer school topics are listed below:

1. Static models: Inferring Typing Behaviour This section looks at building probabilistic Bayesian models of typing behaviour, and inferring parameters using Markov Chain Monte Carlo.

2. Dynamic models: Tracking Cursors and Recognising gestures This section looks at building dynamic probabilistic models that can track states over time, including estimating cursor movement with noisy, partial measurements and spotting and tracking gestures.