Course on Computational Interaction with Bayesian Methods accepted to CHI 2019 in Glasgow

Our course with Per Ola Kristensson (Cambridge), Nikola Banovic (Michigan), and John Williamson (Glasgow) was accepted to CHI 2019 in Glasgow. It’s a 4 unit course. Abstract below:
This course introduces computational methods in human– computer interaction. Computational interaction methods use computational thinking — abstraction, automation, and analysis — to explain and enhance interaction. This course introduces the theory of practice of computational interaction by teaching Bayesian methods for interaction across four wide areas of interest when designing computationally-driven user interfaces: decoding, adaptation, learning and optimization. The lectures center on hands-on Python programming interleaved with theory and practical examples grounded in problems of wide interest in human-computer interaction.
The course will cover:
Decoding: Principled and robust formulations for proba- bilistically decoding noisy sensor observations into user’s intended actions.
Adaptation: Probabilistic adaptation demonstrated by se- quential Monto Carlo approaches for modeling HCI tasks.
Learning: Using inverse reinforcement learning for learning computational models of human behavior.
Optimization: Using Bayesian optimization to perform human-in-the-loop optimization of a user interface problem.